Advancements in Millimeter-Wave Radar Technologies for Automotive Systems: A Signal Processing Perspective

Abstract

This review paper provides a comprehensive examination of millimeter-wave radar technologies in automotive systems, reviewing their advancements through signal processing innovations. The evolution of radar systems, from conventional platforms to mmWave technologies, has significantly enhanced capabilities such as high-resolution imaging, real-time tracking, and multi-object detection. Signal processing advancements, including constant false alarm rate detection, multiple-input–multiple-output systems, and machine learning-based techniques, are explored for their roles in improving radar performance under dynamic and challenging environments. The integration of mmWave radar with complementary sensing technologies such as LiDAR and cameras facilitates robust environmental perception essential for advanced driver-assistance systems and autonomous vehicles. This review also calls attention to key challenges, including environmental interference, material penetration, and sensor fusion, while addressing innovative solutions such as adaptive signal processing and sensor integration. Emerging applications of joint communication–radar systems further presents the potential of mmWave radar in autonomous driving and vehicle-to-everything communications. By synthesizing recent developments and identifying future directions, this review stresses the critical role of mmWave radar in advancing vehicular safety, efficiency, and autonomy.

1. Introduction

Millimeter wave (mmWave) technology operates at high frequencies compared to conventional radio-frequency (RF) systems, where mmWave typically refers to the 30 GHz to 300 GHz range. mmWave technology has seen great improvements in recent decades in light of advancements in semiconductor devices, improved system architectures, increased computational power, etc. Initially, the high cost of mmWave devices posed a significant challenge, but the advanced semiconductor process makes the production of affordable mmWave devices possible. Currently, mmWave is sparking a surge of interest in this area [1,2]. At the same time, increasing the operating frequency offers great potential to achieve higher data rates in communication systems.

mmWave technology is not limited to wireless communications. It has already been used in automotive radars available on the market since the end of the last century [3,4,5]. Its location in the electromagnetic spectrum is shown in Figure 1. Thanks to affordable chip technology and enhanced reliability, mmWave radar sensors have become increasingly popular for civilian uses [6], such as detecting obstacles [7], recognizing motions [8], pinpointing locations [9], tracking objects [10], etc. mmWave radar technology has gained significant traction and popularity, as mmWave can achieve higher resolution due to its shorter wavelengths compared to conventional radio-frequency (RF) radars. This allows for detailed detection and imaging of objects, which is crucial for applications requiring precise spatial information, such as autonomous vehicles and advanced driver assistance systems (ADAS) [11,12]. The exploration of mmWave technologies is essential for advancing vehicular communication systems, which may yield a solution to the increasing demand for high data rates and reliable connectivity in autonomous driving and vehicle-to-everything (V2X) communications [13,14].

Modern vehicles require multi-sensor technique and communication systems to perceive the environment and react to it. Thus, mmWave radars are often integrated with other sensors to augment the ability to extract information under various environments. Moreover, at mmWave frequencies, there has been a growing interest in systems such as radar and LiDAR that can share the same frequency bands as wireless communication technologies, whether radio frequency, optical, or acoustical, without losing effectiveness. This surge in interest is mainly because the frequencies these technologies use are limited, while both communication and remote sensing technologies benefit from having access to a broader range of frequencies.

The terminology of radar signal processing encloses detection theory, performance evaluation, and circuitry [15]. During World War II, there were numinous studies on the design of radar transceivers for better range and resolution using pulsed and continuous-wave (CW) signals. These transmitted signals were simple, and their performances were limited by the devices available at that time. Until about 1955, most of the efforts were on larger-power transmitters, better low-noise amplifiers, and mixers to achieve better signal-to-noise ratio (SNR). When the transmitted power reached a megawatt level, the merit of increasing the power became questionable. After this time, the development of the chirp system and coding of the transmission began.

During the 1950s, the development of signal processing techniques often outpaced the evaluation of their overall impact and effectiveness. By 1962, as highlighted in [16], it became possible to predict the range of completed radar designs to within a 50% accuracy. The advent of the fast Fourier transform (FFT) in 1965 marked a pivotal moment for digital signal processing (DSP) in radar technology, laying the groundwork for what would evolve into the cornerstone of contemporary radar systems. During the subsequent three decades, a surge in advancements was witnessed, fueled by enhanced devices, antennas, and computational capabilities [17]. This period saw the realization of numerous algorithms and systems, setting the stage for the sophisticated mmWave radar applications tailored for vehicular use today.

To provide context for the recent advancements in millimeter-wave radar systems, particularly in automotive applications, Figure 2 illustrates a timeline of key historical milestones in radar technology. Beginning with the introduction of pulsed radars in the 1940s, the field progressed with the invention of FMCW radar in the 1950s, followed by the adoption of fast Fourier transform (FFT) techniques in the 1960s, which enabled more efficient spectral analysis. The first automotive radar patent was filed in 1972, laying the foundation for vehicular applications. Notable milestones also include the introduction of adaptive cruise control in 1992, the standardization of 77 GHz mmWave radar in 1999, and the emergence of MIMO radar systems in the early 2000s, which significantly improved spatial resolution. By 2012, radar had become central to ADAS, and by 2015, commercial deployment accelerated. More recently, deep learning techniques have been applied to radar signal processing, marking a shift toward intelligent and adaptive radar systems.

The exigency for a radar framework that is both flexible and computationally adept has become essential across many industries. These frameworks are tasked with managing extensive target lists, encapsulating thousands of detections. This requirement originates from the escalating complexity inherent in applications that span autonomous vehicular systems, sophisticated traffic management solutions, surveillance operations, and environmental scrutiny. It is imperative that these frameworks not only manage the voluminous data yielded by state-of-the-art radar mechanisms but also exhibit versatility across diverse environmental settings and operational conditions. This ensures the delivery of precise and timely data, crucial for informed decision-making. The prompt processing and analysis of these data are indispensable for ensuring the applications’ safety, operational efficiency, and overall effectiveness. This stresses the critical role that advancements in radar technology and data processing algorithms play in meeting contemporary demands.

This review paper is organized as follows: Section 2 outlines the motivation for this review, emphasizing the rapid advancements in mmWave radar technologies and their transformative applications across industries, particularly in the automotive sector. It shows the evolution of radar systems, the integration of cutting-edge signal processing techniques, and the importance of multi-sensor fusion in enabling precise environmental perception. Section 4 provides a comprehensive overview of state-of-the-art automotive radar signal processing methodologies, including discussions on frequency-modulated continuous wave (FMCW) radar, constant false alarm rate (CFAR) detection, MIMO techniques, and machine learning-based signal processing. Section 5 explains the challenges faced by mmWave radar systems, such as environmental interference, high-frequency limitations, and the need for seamless sensor integration. Lastly, the last section concludes this paper by summarizing the critical insights, discussing the broader implications of mmWave radar in autonomous systems, and identifying future research directions to address current limitations and optimize performance. The flow of this review is given in Figure 3.

2. Motivation

Rapid advancements in millimeter wave radar technologies have significantly transformed their applications across industries, with the automotive sector being a key beneficiary. This review is motivated by the growing importance of mmWave radars in addressing critical challenges in modern systems, their integration potential, and the emergence of innovative solutions for performance optimization. Below, we outline the primary motivations for conducting a comprehensive review of state-of-the-art mmWave radar technologies. The evolution of radar systems from traditional pulsed and continuous-wave platforms to advanced mmWave radar technologies has introduced capabilities such as high-resolution imaging, real-time tracking, and multi-object detection. Innovations in signal processing, such as multiple-input–multiple-output (MIMO) techniques and machine learning-based approaches, have further enhanced the ability of mmWave radars to operate in complex environments. These advancements warrant a thorough review to understand their implications for both automotive and non-automotive applications.

Recent works feature the integration of FMCW radar systems, which utilize chirp sequence modulation for precise range and velocity measurements. The application of MIMO systems, which enable spatial diversity and enhanced resolution, has proven invaluable in dynamic and high-density environments such as urban traffic and autonomous vehicle navigation [18,19]. mmWave radars play an important role in advanced driver-assistance systems (ADAS) and autonomous driving. Their high-frequency operation and shorter wavelengths allow for precise spatial resolution, enabling reliable obstacle detection, blind spot monitoring, and motion recognition. This capability is essential to ensure the safety and efficiency of modern vehicular systems [5,6].

The growing adoption of mmWave radar for automotive applications therefore excites the need for continuous assessment of its technologies. This includes understanding performance improvements achieved through innovations like multi-frame integration for high-resolution imaging and advanced Constant False Alarm Rate (CFAR) detection algorithms [20].

Modern applications increasingly demand multi-sensor fusion to augment the performance of standalone systems. mmWave radars, when integrated with cameras, LiDAR, and ultrasonic sensors, offer comprehensive environmental mapping capabilities. This integration facilitates better decision making in autonomous systems under varying environmental conditions [10]. The ability of mmWave radars to complement other sensors and their adaptability to emerging sensor fusion techniques accentuate their importance in creating robust and versatile systems. Addressing the challenges of seamless integration is a critical research direction.

Although millimeter wave radar technologies exhibit promising capabilities, their high-frequency operation poses challenges such as atmospheric absorption, limited penetration through materials, and susceptibility to environmental interference. For instance, rain, fog, and snow can significantly affect radar performance, particularly in automotive environments [21,22].

A comprehensive review helps to identify these limitations and explore advanced solutions, such as adaptive signal processing and machine learning algorithms, to enhance resilience and reliability under adverse conditions.

Beyond the automotive industry, mmWave radars have shown potential in diverse applications, including traffic management, environmental monitoring, and surveillance. The fusion of radar sensing with communication capabilities—such as in vehicle-to-everything (V2X) communication—opens avenues for cross-disciplinary innovations [23]. Joint communication–radar (JCR) systems, which leverage mmWave radar’s dual functionality, exemplify this potential. These systems improve resource utilization and provide enhanced sensing and communication capabilities for smart cities and autonomous systems [24].

As mmWave radars become more sophisticated, future research must address critical aspects, including hardware miniaturization, power efficiency, and regulatory standards for frequency allocation and interoperability [25]. By analyzing current progress and challenges, this review aims to provide a road map for future innovations on mmWave automotive radars.

3. Current State and Challenges of Automotive mmWave Radars

3.1. mmWave Radar Capabilities

mmWave radar has become a cornerstone sensing modality in modern automotive systems, particularly for its applications in ADAS and autonomous vehicles. Owing to its ability to function under diverse environmental conditions, its long-range detection, and its relatively compact hardware footprint, mmWave radar is increasingly integrated alongside cameras and LiDAR in multi-modal sensor stacks. Commercial automotive radars operating in the 76–81 GHz band are now standard in features such as adaptive cruise control, collision avoidance, lane change assist, and parking support.

High-Resolution Imaging: High-resolution radar imaging is primarily enabled through advanced MIMO radar architectures. Techniques such as time division multiplexing MIMO or FMCW MIMO allow for the construction of virtual arrays, thereby synthetically enlarging the aperture and improving angular resolution. To further enhance the fidelity of range and angle estimation, super-resolution algorithms like MUSIC and ESPRIT are employed, along with more recent approaches involving sparse recovery (e.g., compressive sensing) and deep neural networks. Radar imaging capabilities, such as range–azimuth heatmap generation and three-dimensional cube visualization in the range–azimuth–Doppler domain, benefit from deep learning-based image enhancement methods, including autoencoders and generative models.

Real-Time Tracking: Real-time tracking in mmWave radar systems is typically achieved using filtering techniques such as the Kalman filter, extended Kalman filter, and interacting multiple model filter, which help maintain consistent target trajectories over time. In complex, multi-target environments, joint probabilistic data association and multiple hypothesis tracking methods are employed to resolve measurement-to-target associations. More recently, lightweight recurrent neural networks and transformer-based architectures have emerged as effective solutions for low-latency, frame-to-frame tracking in real-time scenarios. In addition, track-before-detect strategies are being explored to improve performance in low signal-to-noise ratio settings where traditional detection-first pipelines may fail.

Multi-Object Detection: The detection of multiple objects is commonly performed using CFAR detection algorithms, such as ordered statistics CFAR and greatest-of CFAR, particularly within range–Doppler and range–angle maps. In highly cluttered or dynamic environments, adaptive CFAR variants and learning-based thresholding methods have demonstrated superior robustness. With the advent of radar point cloud data, machine learning techniques adapted for radar are increasingly being used for object classification and semantic understanding. Furthermore, clustering algorithms like DBSCAN and mean-shift are applied to spatially group radar returns into coherent objects, serving as a precursor to higher-level tasks such as classification and tracking.

3.2. mmWave Radar Challenges

Despite its growing adoption and proven utility, several key challenges remain in the deployment and optimization of mmWave radar for automotive applications:

Radar systems are susceptible to interference from environmental factors such as wet roads, guardrails, roadside infrastructure, and even other radars operating in adjacent lanes. Multipath reflections, ghost targets, and radar cross-section fluctuations pose significant issues for accurate target identification and localization.

Achieving high range, velocity, and angular resolution often requires wide bandwidths and large antenna arrays (e.g., MIMO configurations), which increase cost, power consumption, and complexity. Balancing performance and cost remains a persistent engineering trade-off, especially for mass-market adoption.

While radar excels at range and velocity estimation, it lacks detailed texture and semantic understanding. For this reason, sensor fusion with cameras and LiDAR is common. However, real-time synchronization, calibration, and fusion of heterogeneous sensor data streams present significant software and hardware integration challenges.

As radar systems generate increasingly high-resolution data, processing at the edge within the vehicle becomes more computationally demanding. Designing efficient signal processing and machine learning pipelines that meet real-time constraints while operating within automotive-grade hardware limitations is an ongoing challenge.

Automotive radars must reliably perform in complex and dynamic environments, such as urban intersections, highway merges, or rural roads with minimal infrastructure. Detecting vulnerable road users such as pedestrians or cyclists, small or partially occluded objects, and fast-approaching vehicles in cluttered scenes is still an area of active research.

The 76–81 GHz band is shared across multiple radar applications. As the number of radar-equipped vehicles increases, inter-radar interference is becoming more common. Cooperative sensing, radar signal orthogonality, and dynamic spectrum management are being explored as possible mitigation strategies.

4. State-of-the-Art Automotive Radar Signal Processing

In the automotive industry, leading-edge radar systems harness the power of frequency-modulated continuous-wave (FMCW) radar. FMCW allows for simultaneous transmission and reception of signals, enabling the radar to measure the range and relative velocity of objects continuously. The transmission of a signal that increases or decreases in frequency over a defined period is known as “chirp”. The radar receives the echo of this chirp from reflecting objects. The time delay between chirp and echo translates into the distance to the object, and the frequency difference between them is proportional to the relative velocity of the object. This frequency difference is caused by the Doppler effect, where the frequency of the returned signal is changed based on the movement of the object towards or away from the radar. The transmitted and received signals are reviewed. In this section, the FMCW radar principles are first introduced as a background, followed by recent development in constant false alarm rate detection, MIMO, and machine learning-based signal processing.

4.1. FMCW Radar Principles

The FMCW radar continuously emits a signal whose frequency is linearly dependent over time in one cycle, which is called “chirping”. An example is given, where the time-domain signal and its spectrogram are shown in Figure 4.

In FMCW radar systems, the transmitted signal is typically a linear chirp that sweeps across a bandwidth B over a sweep duration $T_c$. Below are the key equations and their physical interpretations. The transmitted FMCW signal can be represented as follows:

$$s_{\mathrm{tx}}\left(t\right)=cos\left(2\pi \left(f_{0}t+{\displaystyle \frac{K}{2}}{t}^2\right)\right)$$

(1)

This is the transmitted FMCW signal, where $f_0$ is the starting frequency and $K={\displaystyle \frac{B}{T_c}}$ is the chirp rate (Hz/s).

The received signal is a delayed version of the transmitted chirp due to the round-trip propagation delay $\tau ={\displaystyle \frac{2R}{c}}$, where R is the target range and c is the speed of light.

$$s_{\mathrm{rx}}\left(t\right)=cos\left(2\pi \left(f_0(t-\tau )+{\displaystyle \frac{K}{2}}{(t-\tau )}^2\right)\right)$$

(2)

The beat signal is obtained by mixing (multiplying) the transmitted and received signals. It contains the frequency difference that encodes range information.

$$s_{\mathrm{beat}}\left(t\right)=s_{\mathrm{tx}}\left(t\right)·s_{\mathrm{rx}}\left(t\right)$$

(3)

Assuming $\tau \ll T_c$, the beat frequency $f_b$ is proportional to the target range R.

$$f_b\approx K\tau ={\displaystyle \frac{2KR}{c}}$$

(4)

This equation allows for estimation of the target range by measuring the beat frequency $f_b$.

$$R={\displaystyle \frac{c{f}_b}{2K}}$$

(5)

This is the phase of the received signal, showing how the delay affects the received chirp waveform.

$${\varphi }_{\mathrm{rx}}\left(t\right)=2\pi \left(f_0(t-\tau )+{\displaystyle \frac{K}{2}}{(t-\tau )}^2\right)$$

(6)

The beat phase ${\varphi }_b\left(t\right)$ is used to extract the beat frequency and ultimately range and Doppler information.

$${\varphi }_b\left(t\right)={\varphi }_{\mathrm{tx}}\left(t\right)-{\varphi }_{\mathrm{rx}}\left(t\right)$$

(7)

For moving targets, Doppler frequency $f_D$ introduces an additional shift in the beat frequency. Range and velocity are decoupled using multi-chirp processing (e.g., 2D FFT).

$$f_{b,\mathrm{doppler}}=f_b+f_D$$

(8)

Velocity v is derived from the Doppler frequency $f_D$, where $\lambda$ is the wavelength of the carrier signal.

$$v={\displaystyle \frac{\lambda f_D}{2}}$$

(9)

By analyzing the beat signal over N chirps across $T_m$, one can extract both range and velocity through a range–Doppler map. The parameter $T_m$ (measurement time) refers to the total observation duration over which multiple chirps are sent. It affects Doppler resolution:

$$\Delta f_D={\displaystyle \frac{1}{T_m}}$$

The total frequency difference includes the beat frequency and Doppler shift, which are analyzed via two-dimensional fast Fourier transform (FFT) to extract range and velocity data. Then, the range–Doppler map, generated via FFT processing, can be visualized in a two-dimensional plane, representing range along one axis and velocity along the other.

FMCW incorporates chirp sequence modulation along with stretch processing to achieve distance and speed measurements with unparalleled precision. Such accuracy is indispensable for detecting objects in the vehicle’s vicinity. The radar system’s ability to vary signal frequency according to a set pattern enables the identification and precise location and speed estimation of various objects simultaneously. This feature is integral to the functionality of advanced driver-assistance systems (ADAS) and the burgeoning field of autonomous vehicles, both reliant on the swift and exact evaluation of environmental conditions for optimal safety and navigation. The adoption of chirp sequence modulation, together with advanced signal processing, substantially boosts radar performance. Consequently, FMCW radar emerges as a crucial element in the evolution of smart transportation systems, epitomizing the synergy of technology and safety.

4.2. Constant False Alarm Rate Detection

4.2.1. Overview

Constant false alarm rate (CFAR) detection is a method widely used in radar systems to detect targets by dynamically adjusting the detection threshold to maintain a constant rate of false alarms [26]. The principle behind CFAR is to compare the signal level of a potential target with the average level of surrounding noise or clutter. The detection threshold is set based on this average noise level, typically by multiplying the average by a predefined factor to establish a balance between detecting actual targets and minimizing false alarms, as shown in Figure 5. It is necessary in environments where noise levels vary. CFAR helps distinguish between actual targets and noise, thus ensuring reliable detection performance without a large number of false alarms. CFAR adapts the threshold based on the surrounding noise level, making it an effective tool in the processing of radar signals for various applications [27,28,29,30,31]. Many algorithms and implementations were proposed in this decade, including cascaded [32], multi-step [33], dimension-compressed [34], high-order [35], background-knowledge-based [36] CFAR, etc. In this subsection, some recent works and their working principles are reviewed.

One simple algorithm for CFAR involves cell averaging, where noise samples are extracted from leading and lagging cells around the cell under test. The noise estimate can be computed as follows:

$$P_n={\displaystyle \frac{1}{N}}\sum _{n=1}^N{x}_m$$

(10)

where $x_m$ is the sample in each cell. With the guard cells to protect signal leaking into leading and lagging cells, assuming the data passed into the detector is from a single pulse, i.e., no pulse integration involved, the threshold factor can be written as follows:

$$\alpha =N(P_{fa}^{-1/N}-1)$$

(11)

In Equation (11), $P_{fa}$ denotes the desired false alarm rate. The algorithm of CFAR has variations, where cell-averaging greatest of-CFAR [38], cell-averaging smallest Of-CFAR [39], ordered statistic-CFAR [40], and cell-averaging statistic Hofele-CFAR [41] were implemented in the last three decades.

4.2.2. Recent Works

Recently, Thiagaraja et al. proposed a multi-stage CFAR algorithm for mmWave radars [20], enhancing target detection without requiring noise variance knowledge. This paper introduces an order statistics detector (OSD) for coarse detection, followed by a weighted centroid detector (WCD) for fine analysis. Synthetic and real-world data are shown to prove the effectiveness of the proposed method. This two-stage process enables more precise target identification, especially in environments with high clutter or noise. The approach provided in this paper significantly improves detection performance, offering a promising solution for advanced radar systems in challenging environments.

In 2023, Liang et al. proposed a novel approach for plotting the false alarm–threshold relationship curve for radar detectors, combining piecewise parabolic interpolation with importance sampling [42]. This method efficiently evaluates false alarm probabilities and detector thresholds, especially beneficial for radar systems with extremely low false alarm rates. By employing piecewise parabolic interpolation and importance sampling, the method improves the accuracy of evaluating low false alarm probabilities. This approach optimizes the calculation process, making it more effective for radar systems that require the precise determination of detector thresholds at very low false alarm rates. Through enhanced sample efficiency and reduced computational demands, the proposed technique ensures more accurate and reliable evaluations, crucial for the optimal performance of radar detection systems in scenarios where minimizing false alarms is paramount.

By utilizing multi-frame integration in heavy-tailed clutter environments, Cao and Zhao proposed an improved CFAR detector [43]. This work focuses on fluctuating target detection with high resolution and minimal grazing angles. By deriving closed-form expressions for the probability of detection and false alarm rates, the study introduces a CFAR detector designed to effectively handle target-like outliers and alleviate the masking effect in multi-target scenarios. This approach demonstrates superior performance in signal-to-clutter ratio enhancement over single-frame-based detectors, offering significant advancements in radar detection capabilities in challenging clutter backgrounds.

In a recent work, Li et al. proposed a new semi-parametric CFAR method tailored for radar and sonar imaging using a Gaussian mixture model (GMM) for enhanced detection [44]. GMM is a probabilistic model that assumes all the data points are generated from a mixture of a finite number of Gaussian distributions with unknown parameters. Using the expectation-maximization algorithm, the GMMs iteratively adjust the parameters of the Gaussian parameters, including the mean, covariance, and the mixture coefficient for each component to maximize the likelihood of the data. By employing the Gabor wavelet, a mathematical function used in image processing and computer vision for analyzing spatial frequencies and orientations to determine the optimal GMM components, this approach adeptly handles the challenges posed by non-positive definite outputs in polarimetric synthetic aperture radar images. This method offers a more adaptable and precise framework for handling the complexities of modern radar and sonar data analysis.

Wang et al. [45] proposed adaptive optimization to enhance the radar jamming effect. The paper focuses on multi-false target jamming waveforms using genetic algorithms, which is a type of evolutionary algorithm used for optimization, and phase modulation for radar countermeasures. By adjusting phase modulation and waveform parameters in real-time, the approach offers flexibility in countering modern radar systems. The approach in this work allows for the automatic generation and refinement of jamming waveforms, optimizing their effectiveness against radar systems by adjusting parameters such as phase modulation in real time to adapt to various CFAR settings and radar threats. The methodology demonstrates effective suppression of false targets in various CFAR settings.

Table 1. Reviewed papers on CFAR techniques.

Reference	Year	Methodology	Key Features	Application Context
Thiagarajan et al. [20]	2022	Multi-stage CFAR	Coarse (OSD) + Fine (WCD) detection	High-clutter environments
Liang et al. [42]	2023	Threshold optimization	Importance sampling for low false alarm rates	Low false alarm scenarios
Cao and Zhao [43]	2023	Multi-frame integration CFAR	Enhanced detection in heavy-tailed clutter	Fluctuating target detection
Li et al. [44]	2024	Semi-parametric CFAR	GMM-based threshold adaptation	SAR imaging
Wang et al. [45]	2023	Adaptive Optimization CFAR	Genetic algorithms for threshold dynamics	Radar countermeasures
Del Prete et al. [32]	2023	Cascaded CFAR	Multi-frequency SAR integration	Complex environments
Rosu [34]	2023	Dimension-compressed CFAR	Reduced computational complexity	Massive MIMO radar systems
Liu et al. [35]	2023	High-order CFAR	Weak target detection enhancement	Weak target scenarios
Liu et al. [36]	2023	Clutter Knowledge-based CFAR	Adaptive clutter-aware thresholds	Cognitive radar applications
Shen et al. [33]	2023	Two-step CFAR	Coarse and refined target detection	3D point cloud extraction
Li et al. [31]	2023	Integrated detection and imaging CFAR	Sparse target improvement with ADMM	Radar sparse target detection
Sim et al. [27]	2023	FPGA-Based CFAR	Real-time hardware efficiency	FPGA-based radar systems
Roldan et al. [30]	2024	Data-driven CFAR	LiDAR-based radar enhancement	Data-driven radar detection

provides a summary of recent developments in CFAR.

4.2.3. Critical Analysis

CFAR detection techniques are widely employed in mmWave automotive radar systems. Although CFAR is critical for adaptive thresholding in target detection, there are more spaces to explore:

• Dynamic Clutter Environments: Existing CFAR algorithms struggle to adapt effectively in highly dynamic and heterogeneous clutter environments, such as urban scenarios with dense traffic and infrastructure reflections. Current solutions do not adequately handle the fast varying noise levels.
• Multi-Target Resolution: Many CFAR implementations have limited capability in resolving closely spaced or overlapping targets, leading to detection ambiguity in multi-object environments. This gap is critical for automotive safety applications, where precise detection is essential.
• Computational Complexity: Advanced CFAR algorithms, including multi-stage and high-order variants, often involve increased computational load, limiting their real-time deployment in automotive systems with limited computational resources.
• Integration with Machine Learning: Limited research exists on integrating CFAR with machine learning techniques for intelligent threshold adaptation, which could significantly improve detection accuracy in complex environments.

Overcoming limitations related to dynamic clutter adaptation, multi-target resolution, and computational complexity will enable more precise and consistent target detection in complex driving environments. This progress is critical for supporting the development of autonomous vehicles, where reliable real-time detection is essential for safety and operational/cost efficiency.

4.3. MIMO Techniques for Performance Enhancement

4.3.1. Overview

Today, MIMO systems are widely utilized to increase the number of antennas, thus achieving higher gains. MIMO allows the system to send and receive multiple signals simultaneously over the same radio channel by exploiting spatial diversity. This significantly enhances the radar system’s ability to resolve targets in both angle and range, providing a more detailed and accurate picture of the environment. By leveraging MIMO technology, automotive radar systems can be better in detecting and classifying objects, even in complex scenarios with multiple, closely situated targets. This increased resolution and sensitivity are crucial for advanced safety features, such as adaptive cruise control, collision avoidance, and autonomous navigation.

There are many research papers on multichannel digital transmission systems and interference [46,47,48]. MIMO works by transmitting multiple data streams simultaneously over the same radio channel, exploiting spatial and antenna diversity. This enhances the capacity and reliability of wireless networks, allowing for higher data rates and more stable connections without requiring additional bandwidth [49]. Also, MIMO systems enjoys higher antenna gain from beam-steering [50,51]. In radar systems, MIMO allows for the simultaneous transmission and reception of multiple signals using multiple antennas, creating a multidimensional, high-resolution view of the environment [52,53]. In mmWave radars, MIMO further enhances radar performance by improving the resolution and accuracy of detected objects at higher frequencies. There are many real-life applications, including human activity classification [54], multi-target localization [55], group people counting [56], imaging [57], activity classification [58], etc. This is particularly beneficial for automotive radars, where accurately detecting and tracking objects in real-time is crucial for safety and navigation [59,60,61,62,63]. In this subsection, recent works on mmWave MIMO system architectures and signal processing algorithms are reviewed.

4.3.2. Recent Works

Recently, Ozkaptan et al. introduced a MIMO OFDM-based joint radar-communication (JRC) system for 24 GHz mmWave radars, showing a novel radar-assisted pre-coding method for improved MIMO radar processing and single-user communication [18]. By leveraging real-time radar measurements for low-overhead pre-coding, this work advances beyond traditional approaches, emphasizing low complexity without requiring channel state information feedback, where the pre-coding technique proposed in this work is a technique used in MIMO communications to enhance signal strength and quality at the receiver end by pre-adjusting the transmitted signals based on the channel’s characteristics. Integrating radar data allows for more accurate and efficient adaptation to the environment, improving both radar detection capabilities and communication performance in mmWave bands.

In 2019, Wang et al. firstly presented a significant advancement in integrating wireless communication and synthetic aperture radar (SAR) imaging through an airborne MIMO radar system [64]. By moving a radar antenna over a target area to simulate a larger antenna aperture, SAR generates high-resolution images. By analyzing the time delay and frequency changes of radar reflected signals, SAR can construct detailed 2D or 3D representations of landscapes. The signal flow diagram and system architecture are shown in Figure 6. Wang demonstrates the ability to perform high-resolution SAR imaging while simultaneously transmitting wireless communication signals, overcoming traditional challenges such as intra-modal interference and optimizing system performance for both functionalities.

Following Wang’s work, in a recent work by Gao et al., the combination of MIMO and SAR is achieved [65]. This new approach effectively combines the broad detection capabilities of MIMO processing with the high-resolution imaging ability of SAR. By combining MIMO processing for initial target detection with SAR processing for detailed imaging, this work focuses only on regions of interest to reduce the computational load. This innovative approach enhances azimuth resolution and object separation. It leverages radar odometry for accurate motion estimation, facilitating coherent image processing over multiple frames. Therefore, it significantly improves the azimuth resolution and the capability to distinguish between closely spaced objects.

Kumari et al. introduced an adaptive virtual waveform design for mmWave JCR systems [66], optimizing the trade-off between radar performance and communication efficiency. Waveform design in mmWave systems for JCR operations indirectly enhances radar detection by optimizing the signal characteristics. Specifically, by tailoring the waveform, the system can better manage the trade-offs between radar sensing accuracy and communication data rates. Waveform design indirectly impacts the radar detection capability by optimizing signal characteristics. This optimization includes adjusting the signal parameters to improve the radar’s ability to detect and estimate the velocity of targets, which directly impacts the system’s overall detection capability by making it more efficient and accurate in various operational scenarios.

The same group published their work later in 2021 in mmWave JCR systems for automotive applications with both adaptive and fast beamforming and waveform design [19]. The system is optimized for both communication and radar sensing. This is achieved through a phased-array architecture with phase-only control. The phase-only topology simplifies hardware requirements and at the same time reduces power consumption. The fast-beamforming allows transceivers to adjust how signals are sent and received quickly and efficiently. This allows the system to better focus on specific targets for radar detection while maintaining clear communication channels. In literal and figurative language, the radar works by fine-tuning a radio to obtain the best signal without interference, but this is done very quickly and automatically to adapt to changing conditions. It also enhances system performance in Doppler-angle domain radar channel estimation while minimally impacting communication data rates. The methodology emphasizes a balance between radar accuracy and communication efficiency, leveraging compressed sensing techniques for rapid estimation and minimal coherence optimization for enhanced performance across dynamic road scenarios.

A recent study [67] introduces simultaneously transmitting and reflecting reconfigurable intelligent surfaces to enhance dynamic scatterer tracking performance in ISAC-enabled systems. This architecture allows radar systems to adaptively control propagation paths, improving both sensing precision and communication reliability. Future research should explore how such reconfigurable environments, when combined with advanced signal processing and machine learning, can address challenges in real-time tracking, occlusion handling, and cooperative vehicle perception.

Recently Li et al. [68] investigated advanced signal processing strategies for automotive radar systems, focusing on enhancing the accuracy and reliability of vehicle detection and tracking. They investigated time-division multiplexing (TDM) and MIMO techniques applied to FMCW radars. By applying range-DFT, Doppler-DFT, and angular-DFT sequentially, the radar shows advanced ability in detection, classification, localization etc.

In 2021, Li et al. leveraged mmWave massive MIMO techniques [69] to successfully address the computational challenges in high-resolution environment sensing for unmanned systems. In the paper, a fast randomized-MUSIC (R-MUSIC) algorithm that significantly reduces time complexity while maintaining high resolution and accuracy in angle-of-arrival (AoA) estimation is proposed. It is achieved through random matrix sketching to approximate the signal subspace. The method resolves the long-standing contradiction between computational complexity and accuracy, validated through numerical simulations.The R-MUSIC algorithm attains the same accuracy as the near-optimal MUSIC method, while at the same time, it resolves the inherent contradiction between complexity and accuracy problems.

Zhang et al. presented a system for joint communication and radar sensing (JCAS) using multibeam techniques [70]. The JCAS is illustrated in Figure 7. This approach enables simultaneous communication and radar sensing with flexible, high-resolution capabilities. It offers significant advantages over traditional JCAS methods by providing a seamless integration of communication and sensing functions, optimizing the use of hardware and spectrum resources.

Based on Zhang’s work, building on the concept of utilizing advanced antenna technologies to improve integrated sensing and communication (ISAC) systems, Gao et al. [24] proposed an innovative framework for ISAC using mmWave massive MIMO systems. This work focuses on using compressed sampling to enhance ISAC processing, enabling efficient recovery of high-dimensional channel and radar information and reducing pilot overhead. An energy-efficient architecture and a novel ISAC frame structure designed to handle time-varying systems is proposed. It demonstrates significant improvements in both communication and radar sensing performance in numerical simulations. The work by Gao et al. also shows how radar-assisted techniques, such as predictive beamforming, can be utilized in mmWave massive MIMO systems to enhance vehicular communication networks.

Predictive beamforming is presented in Liu’s work [71]. In this paper, a novel radar-assisted predictive beamforming method is proposed for vehicle-to-infrastructure (V2I) communication. The system utilizes dual-functional radar-communication techniques to enhance both sensing and communication functionalities. A dual-functional radar-communication system is designed to perform both radar sensing and wireless communication simultaneously using the same hardware infrastructure. The paper employs an extended Kalman filtering framework to predict kinematic parameters of vehicles. For applications, a simple example could be a scenario where a vehicle uses its radar system not only to detect obstacles and other vehicles for safety but also to communicate with other vehicles and roadside infrastructure. This integration allows for efficient use of spectrum and hardware to improve overall system performance in terms of safety, traffic management, and information exchange among vehicles and roadside units. The extended Kalman filtering technique predicts vehicle positions and velocities, making it possible for the system to anticipate the optimal directions for beamforming. The performance is depicted in Figure 8.

Compressive sampling allows the MIMO system to reconstruct target information from significantly fewer samples than traditional methods would require [72,73]. Yu et al. first developed a system that operates in a network of randomly distributed nodes, transmitting uncorrelated waveforms and utilizing compressive sampling at the receiver end to minimize the sample count needed for effective target information retrieval [74]. The approach by Yu demonstrates the ability to achieve high-resolution MIMO radar performance with significant reductions in power and computational resource needs, even in scenarios with jamming signals.

The method proposed in [72] achieves higher efficiency by reducing hardware requirements and improving the mathematical framework. In this work, the FMCW signal processing is modeled in a tensor format, facilitating systematic analysis. Additionally, the sparsity feature of FMCW radar data is mathematically analyzed, where the inherent sparsity of radar signals in certain domains is leveraged to minimize the amount of data that need to be collected, processed, and stored. The process is shown in Figure 9. As a result, computational load and resource requirements are reduced.

The study in [73] introduces an off-grid compressive sensing algorithm, named refinement and generalized double Pareto distribution based on sparse Bayesian learning. This method integrates sparse Bayesian learning with grid refinement to enhance the estimation accuracy of range, velocity, and other target parameters. The off-grid issues are mitigated using a two-stage search approach that improves estimation accuracy while maintaining low computational complexity.

Table 2. Comprehensive summary of reviewed MIMO techniques.

Reference	Year	Methodology	Key Features	Application Context
Ozkaptan et al. [18]	2023	Radar-assisted precoding in JRC systems	Optimized detection and communication with low overhead	Joint radar–communication systems
Wang et al. [64]	2019	MIMO-SAR integration	High-resolution imaging and simultaneous comm.	Imaging in and comm. autonomous systems
Gao et al. [65]	2021	Region-of-interest SAR processing	Reduced computational load	Efficient autonomous radar systems
Kumari et al. [66]	2019	Adaptive waveform and beamforming	Optimized radar–communication	Dynamic automotive scenarios
Li et al. [69]	2021	Fast randomized-MUSIC algorithm	Efficient angle estimation with reduced complexity	High-resolution angle estimation
Yu et al. [74]	2010	Compressed sampling in MIMO radar	Resource-efficient high-resolution performance	Low-resource autonomous systems
Zhang et al. [70]	2018	Steerable analog antenna arrays in JCAS	Seamless integration of communication and sensing functions	Integrated sensing and communication systems
Gao et al. [24]	2022	Compressed sampling for ISAC systems	Efficient recovery of high-dimensional radar and channel information	Integrated sensing and communication (ISAC) systems
Liu et al. [75]	2020	Predictive beamforming for V2I communication	Enhanced radar and communication functionality using Kalman filtering	Vehicle-to-infrastructure communication
Li et al. [68]	2021	TDM and MIMO for FMCW radars	Detection, classification, and localization	Automotive FMCW radar systems
Rahayu et al. [72]	2025	Compression and reconstruction	High efficiency and power reduction	Range–velocity–AoA Estimation
Wang et al. [73]	2024	Off-grid compressive sensing	Computational cost reduction	Radar with limited computational power
Li et al. [67]	2025	Dynamic scatterer tracking	Beam prediction for both pre-coder of base station and the refraction phase shift vector	Integrated sensing

provides a comprehensive summary of the MIMO mmWave radar techniques reviewed.

In summary, these recent papers collectively demonstrate the advancements in MIMO radars and their applications in the automotive industry. They introduce various methodologies to enhance mmWave radar resolution, accuracy, and efficiency with advanced signal processing techniques. These advances enable more precise object detection, vital sign monitoring, and environmental awareness of vehicles, paving the way for smarter and safer automotive solutions on high-frequency, high-precision technologies.

4.3.3. Critical Analysis

MIMO techniques enhance the performance of radars in mmWave automotive radar systems. Despite significant advancements, several research gaps and inconsistencies are still existing:

• Hardware Complexity and Cost: The deployment of large-scale MIMO systems significantly increases hardware complexity and production costs. Current research does not adequately address cost-effective and scalable designs for mass-market automotive applications.
• Real-time Processing Limitations: In terms of computational cost, higher-dimensional signal processing tasks—such as beamforming, angle-of-arrival (AoA) estimation, and clutter suppression—become more demanding as the MIMO grid expands. Efficient implementation is essential to meet real-time constraints in embedded automotive systems.
• Beamforming and Interference Mitigation: Interference mitigation in MIMO radar becomes increasingly important in dense vehicular environments where multiple radars operate simultaneously. Strategies such as code-division multiplexing, frequency-hopping, orthogonal waveform design, and adaptive beamforming have been explored to reduce cross-radar interference and false alarms. Additionally, cooperative radar networks may adopt sensor scheduling or dynamic resource allocation to manage spectral overlap in shared radar–communication bands.
• Integration with JCR: While some studies address joint radar–communication systems, the seamless integration of MIMO radar with V2X communication for dynamic and high-mobility environments remains underdeveloped.

Resolving these gaps is essential to realize robust, efficient, and cost-effective MIMO mmWave radar systems in autonomous driving and ADAS. MIMO techniques continue to play an important role in mmWave radar applications, with research focusing on improving array designs, signal processing algorithms, and integration with machine learning for enhanced situational awareness. Future work aims to address challenges related to real-time processing, cost-effectiveness, and scalability for widespread adoption in autonomous vehicles and intelligent transportation systems.

4.4. Machine Learning-Based Radar Signal Processing

4.4.1. Overview

Traditional methods for identifying radar radiation source signals face challenges such as low accuracy and extended operating times [76,77]. However, advancements in artificial intelligence, specifically machine learning, alongside pattern recognition techniques, have led to significant improvements in this domain [78]. With the rapid advancement of machine learning (ML) and deep learning (DL) in the last decade, these techniques are becoming increasingly popular in the field of radar. Although machine learning technology relies heavily on human machine learning experts for radar signal recognition [75], the cross-discipline research area has attracted a large number of dedicated researchers eager to explore the synergies between ML advances and radar technology due to the lower cost of training and the wide scope of applications [79,80,81,82,83,84]. In this subsection, recent work on machine learning-based signal processing is reviewed.

4.4.2. Recent Works

In 2023, Zhao et al. introduced CubeLearn, a novel preprocessing module to improve human motion recognition using raw mmWave radar signals [8]. Unlike traditional methods that rely on DFT preprocessing, CubeLearn utilizes a complex-weighted learnable preprocessing approach, bypassing DFT preprocessing steps. Figure 10 shows how the module replaces conventional DFT and the neuron network for mmWave FMCW radar. Deep learning is leveraged to improve the accuracy of this estimate from noisy radar data, where the model can learn to filter out noise and identify underlying patterns in the radar signals, making the covariance estimation more reliable. This allows for direct feature extraction from raw signals, facilitating an end-to-end deep learning framework specifically designed for motion recognition tasks. Through extensive testing, CubeLearn has demonstrated its ability to improve classification accuracy. CubeLearn achieved an accuracy of 97% on the radar-based gesture classification dataset, with an improvement of 10–15% for simple models, outperforming comparable conventional DFT pipeline models and requiring fewer training epochs. It particularly benefits simpler models suitable for edge devices.

Recently, Graff et al. proposed a passive radar and deep learning algorithm to configure mmWave communication links in multi-user vehicular environments [23]. Radar covariance estimation is applied, which involves using radar signals to infer the spatial characteristics of the environment in order to configure mmWave communication links more efficiently. Deep learning is then used to process the radar data to predict the optimal communication link configurations. By analyzing the radar-derived spatial covariance data, this work shows a complex neuron network to optimize mmWave communication links in V2I setups.

Edge computing is one of the most important applications in machine learning. Sonny et al. recently presented a mmWave radar, integrating ensemble-based classifiers with embedded feature selection to enhance object detection in real-time scenarios [85], shown in Figure 11. The machine learning algorithm interprets the mmWave radar data, leveraging ensemble-based extra tree classifiers to enhance object detection accuracy. The extra tree combines classifiers that use multiple decision trees to improve prediction accuracy for object detection tasks by creating a multitude of decision trees from random subsets of the feature set and then averaging their predictions to improve the overall performance and robustness against noise in the radar data. Overall, this study demonstrates good performance in the detection of open-carried objects, validated against various deep learning models and deployed on edge computing platforms.

Ranging and classification using radar-generated images has been an unavoidable topic since the development of radar systems. In the work by Gupta et al. [86], a target classification using mmWave radar and machine learning is implemented. The system employs YOLO v3, shown in Figure 12, for object detection from radar-generated range-angle images. The YOLO v3 network in this paper includes 53 convolutional layers, known as Darknet-53, followed by additional layers for detection. The YOLO v3 network improves upon previous versions by using a multi-scale detection technique and a deeper, more complex network architecture. Dividing the image into a grid predicts bounding boxes and probabilities for each grid cell. This structure enhances its ability to detect small and varied objects on different scales compared to other networks, making it suitable for vehicular applications where rapid and accurate object detection is critical. The system achieves real-time object detection that processes images in a single pass, making it faster than algorithms that perform detection in multiple steps.

Cenkeramaddi et al. presented a novel machine learning approach to enhance angle estimation and field of view for mmWave FMCW radars [87]. Using a novel algorithm that utilizes range FFT for the estimation of AoA, it achieves a root mean square error (RMSE) of 2.56 degrees in the enhancement of the azimuth and elevation FoV. It integrates machine learning with mmWave radar by taking the estimated angle and estimated range as its input and predicts the actual angle in a polynomial regression model. This work has a complexity of $Nlog\left(N\right)$, which is lower than other works given that there is no limitation on the number of targets. The synergy enhances the radar’s ability to accurately determine the direction of objects.

The integration of deep learning techniques with sensor fusion has significantly enhanced the robustness and accuracy in autonomous systems. Cheng et al. proposed a deep learning-based MOT method that effectively combines data from mmWave radar and cameras to overcome the limitations of single-sensor tracking [88]. The proposed framework employs a bi-directional LSTM network to capture long-term temporal dependencies, thereby improving motion prediction accuracy. Additionally, the adoption of a FaceNet-derived appearance feature model facilitates robust object re-identification across frames, reducing the occurrence of identity switches.

Table 3. Reviewed papers on ML-based algorithms.

Reference	Year	Methodology	Key Features	Application Context
Zhao et al. [8]	2023	CubeLearn with learnable preprocessing	Enhanced motion recognition on edge devices	Human motion recognition
Graff et al. [23]	2023	Radar covariance with DL	Optimized V2I communication links	Multi-user vehicular systems
Sonny et al. [85]	2023	Extra tree classifiers	Real-time object detection with robust accuracy	Edge computing
Gupta et al. [86]	2021	YOLO v3 on radar images	Real-time multi-scale object detection	Vehicular applications
Cenkeramaddi et al. [87]	2021	Polynomial regression for AoA estimation	Accurate angle and FoV enhancement	Automotive radar systems
Bhatia et al. [84]	2021	Ensemble-based classifiers for radar data	Enhanced edge-based object detection	Edge computing
Liu et al. [71]	2020	Dual-functional radar–communication	Improved V2I communication and sensing	Vehicular networks
Li et al. [69]	2021	Fast randomized-MUSIC for AoA estimation	High accuracy with reduced computational costs	Real-time radar applications
Cheng et al. [88]	2024	Integration of deep learning techniques with sensor fusion	Improved robustness in low-visibility scenarios	Multi-object tracking

provides a summary of recent developments in machine learning-based radar signal processing algorithms.

4.4.3. Critical Analysis

While radar has seen various advances in integrating machine learning and deep learning with radar signal processing, several research gaps and inconsistencies emerge.

• Limited Generalization Across Diverse Environments: Most machine learning models are trained and tested in controlled environments. Their robustness and generalization to diverse, dynamic real-world conditions (e.g., varying weather, traffic density) remain under-investigated.
• Data Scarcity and Real-Time Constraints: The reliance on large labeled datasets to train ML models presents challenges, particularly in scenarios requiring real-time processing. Existing approaches inadequately address data acquisition strategies and efficient learning with limited data.
• Edge Deployment Efficiency: While some models aim to optimize edge device performance, there is not enough discussion on the balance of model complexity, latency, and energy efficiency for real-time automotive applications.
• Sensor Fusion Integration: Although mmWave radars are increasingly integrated with other sensors (LiDAR, cameras), how ML models can be designed for seamless multi-modal sensor fusion has not been extensively explored, which is critical for enhanced situational awareness.

Addressing these gaps is essential for developing robust, scalable, and secure mmWave radar solutions for autonomous and driver-assistance systems.

5. Future Challenges

The challenges for mmWave radars in signal processing primarily revolve around their operation at high frequencies. Enabling high-resolution imaging and large bandwidths also introduce significant obstacles. The limited ability of mmWave frequencies to penetrate materials and their susceptibility to atmospheric absorption pose significant challenges, especially in adverse weather conditions such as fog, rain, or snow, which are common in automotive environments. Future research needs to investigate advanced algorithms and signal processing techniques that enhance the resilience of mmWave radars to these environmental factors, ensuring reliable performance regardless of weather conditions [21,22,89].

As the automotive industry and other sectors increasingly adopt mmWave technology, the potential for interference in both intra- inter-system increases. Effective interference mitigation strategies are crucial for maintaining the integrity of radar signals. This includes the development of sophisticated spectrum sharing and coexistence mechanisms that allow mmWave radars to operate efficiently alongside other mmWave-based systems, such as V2X communications and cellular networks. Maximizing the effectiveness of mmWave radars in automotive applications involves their seamless integration with other sensors like LiDAR, cameras and ultrasonic sensors. This sensor fusion approach demands the development of robust algorithms capable of synthesizing data from various sources. As mmWave radar systems become more sophisticated, incorporating capabilities such as detailed environment mapping and classification, concerns regarding security and privacy emerge. It is essential to develop secure communication protocols and data protection measures to safeguard the information collected by these radars.

The demand for high-resolution imaging and classification capabilities continues to grow, driven by the needs of ADAS. Achieving this requires not only improvements in hardware but also advancements in signal processing algorithms and machine learning models that can extract more accurate and detailed information from radar signals, facilitating better object recognition, classification, and tracking [90].

As vehicles incorporate an increasing number of sensors and electronic systems, the importance of power efficiency and the miniaturization of components, including mmWave radars, becomes paramount [25]. Research efforts need to focus on optimizing the design and operation of mmWave radar systems to minimize their power consumption and physical footprint without compromising performance.

The widespread adoption of mmWave radars in automotive applications necessitates clear regulatory guidelines and standards to ensure safety and interoperability. Efforts must be directed towards establishing international standards that address frequency allocation, power limits, safety protocols, etc. for mmWave radar use in vehicles [91].

While mmWave radar systems face numerous technical and practical challenges, recent advancements in signal processing offer several promising avenues for resolution.

Table 4. Challenges, solutions, and advanced methods from a signal processing perspective.

Challenges	Solution	More Advanced Method
Environmental interference and dynamic clutter	Adaptive and learning-based CFAR algorithms, including online threshold adjustment and clutter-aware filtering using spatio-temporal features	Tensor decomposition and dynamic clutter mapping for suppressing interference in high-mobility scenes.
MIMO radar complexity	Compressive sensing and sparse recovery techniques reduce the need for dense antenna arrays while maintaining resolution.	Hybrid analog–digital beamforming with low-rank matrix recovery for scalable MIMO implementations
Multi-target tracking and occlusion handling	Deep multi-object tracking (DeepMOT), attention-based recurrent models, and real-time Kalman filter variants tailored for radar data streams	Graph-based data association using point cloud connectivity to manage occlusions and merge fragmented tracks.
Sensor fusion and latency bottlenecks	Low-latency early fusion methods combined with signal-level preprocessing to reduce computation time before decision-level fusion	Edge-optimized neural networks (e.g., SqueezeRadarNet) paired with pre-Fusion FFT and clustering for fast feature extraction
Radar cross-talk	Orthogonal waveform design (e.g., code-division or frequency-hopping) and mutual interference prediction models.	Advanced method: Adaptive waveform shaping using reinforcement learning to avoid active interference zones in real-time.

summarizes the challenges and proposed solution.

Addressing these challenges requires a multidisciplinary approach that combines advances in hardware, signal processing, machine learning, and regulatory frameworks. Collaborative efforts between academia, industry, and regulatory bodies will be key to overcoming these obstacles, paving the way for the next generation of mmWave radar technology in automotive applications.

6. Conclusions

In this review, we have investigated the significant strides made in the domain of mmWave radar technologies with a focus on their applications in the automotive industries. Through a detailed examination of state-of-the-art signal processing techniques, including CFAR detection, MIMO, and machine learning-based approaches, this paper addresses the critical role of mmWave radars in enhancing the safety, efficiency, and autonomy of modern vehicles.

The evolution from conventional radar systems to advanced mmWave radars marks an unfavorable shift towards achieving higher resolution, more accurate object detection and classification, and improved communication capabilities. This transition is essential in the development of sophisticated driver-assistance systems and the realization of fully autonomous vehicles. Moreover, the integration of mmWave radar with other sensor technologies and the adoption of innovative signal processing algorithms have broadened the potential applications of radar technology in the automotive industry.

Future challenges, such as material penetration, atmospheric absorption, interference management, and the need for sensor fusion, bring up the ongoing need for research and development in this field. Addressing these challenges will require multidisciplinary efforts to enhance the performance, reliability, and safety of mmWave radar systems.

The advancements in mmWave radar technologies offer promising opportunities for the automotive industry and beyond. Continued innovation in signal processing, machine learning, and sensor integration will be crucial in overcoming existing limitations and unlocking the full potential of mmWave radars. As the demand for safer, more efficient, and autonomous vehicles grows, the role of mmWave radars in meeting these demands will become increasingly significant.