Search Results (4)

Frequency-Modulated Continuous-Wave (FMCW) millimeter-wave (mmWave) radars, originally developed for automotive applications, can be also explored for environmental sensing due to their compact size, low cost, and robustness under adverse environmental conditions. However, measurements obtained from commercial automotive radars are often affected by environmental noise and intrinsic self-interference caused by coupling between transmitting and receiving patch antennas, which can degrade the reliability of relative power-based range profiles. In this paper, the performance of the AWR1843BOOST FMCW mmWave radar from Texas Instruments is investigated, with particular emphasis on noise due to antenna coupling. A sub-optimal post-processing technique based on Noise Environmental Measurement (NEM) removal is proposed to remove both deterministic noise, associated with antenna coupling, and stochastic noise, related to environmental contributions. The proposed approach is validated through controlled laboratory experiments involving different targets characterized by distinct dielectric properties, including a metallic object, an absorbing object, and a target with varying degrees of wetness. The experimental results demonstrate that the NEM removal technique significantly enhances the clarity of the backscattered target’s relative power, preserving differences between target values, and improves the radar’s sensitivity to material properties and water content. Measurements accomplished at the electromagnetic and remote sensing laboratory of the Università degli Studi di Napoli Parthenope confirmed the soundness of the proposed NEM removal technique and the sensitivity of the AWR radar to the dielectric properties of targets. Full article

In automotive millimeter-wave (MMW) radar systems, achieving high-precision direction of arrival (DOA) estimation with a limited number of array elements is a crucial research focus. Compressive sensing (CS) techniques have been demonstrated to offer superior performance in DOA estimation compared to spectral estimation methods. However, traditional CS methods suffer from an off-grid effect, which causes their reconstruction results to deviate from the actual positions of the signal sources, thereby reducing the accuracy. Currently, as a gridless method, atomic norm minimization (ANM) has shown effectiveness in DOA estimation for uniform linear arrays (ULAs). However, the performance of ANM is suboptimal in non-uniform linear arrays (NULAs), and their computational efficiency is not satisfactory. In this paper, we propose a novel algorithm for DOA estimation in NULA, drawing inspiration from the alternating descent conditional gradient algorithm framework. First, we construct an atomic set based on the observation scene and select the atoms with the highest correlation to the residuals as potential signal sources for global estimation. Then, we construct a mapping function for the signal sources in the continuous domain and perform conditional gradient descent in the neighborhood of each signal source, addressing the bias introduced by the off-grid effect. We compared the proposed algorithm with ANM, Iterative Shrinkage Thresholding (IST), and Multiple Signal Classification (MUSIC) algorithms. Simulation experiments validate that the proposed algorithm effectively addresses the off-grid effect and is applicable to DOA estimation in coprime and random arrays. Furthermore, real data experiments confirm the effectiveness of the proposed algorithm. Full article

Automotive millimeter-wave (MMW) synthetic aperture radar (SAR) systems can achieve high-resolution images of detection areas, providing environmental perceptions that facilitate intelligent driving. However, curved path is inevitable in complex urban road environments. Non-uniform spatial sampling, brought about by curved path, leads to cross-coupling and spatial variation deteriorates greatly, significantly impacting the imaging results. To deal with these issues, we developed an Extended Omega-K Algorithm (EOKA) for an automotive SAR with a curved path. First, an equivalent range model was constructed based on the relationship between the range history and Doppler frequency. Then, using azimuth time mapping, the echo data was reconstructed with a form similar to that of a uniform linear case. As a result, an analytical two-dimensional (2D) spectrum was easily derived without using of the method of series reversion (MSR) that could be exploited for EOKA. The results from the parking lot, open road, and obstacle experimental scenes demonstrate the performance and feasibility of an MMW SAR for environmental perception. Full article

Automotive millimeter-wave (MMW) radar is essential in autonomous vehicles due to its robustness in all weather conditions. Traditional commercial automotive radars are limited by their resolution, which makes the object classification task difficult. Thus, the concept of a new generation of four-dimensional (4D) imaging radar was proposed. It has high azimuth and elevation resolution and contains Doppler information to produce a high-quality point cloud. In this paper, we propose an object classification network named Radar Transformer. The algorithm takes the attention mechanism as the core and adopts the combination of vector attention and scalar attention to make full use of the spatial information, Doppler information, and reflection intensity information of the radar point cloud to realize the deep fusion of local attention features and global attention features. We generated an imaging radar classification dataset and completed manual annotation. The experimental results show that our proposed method achieved an overall classification accuracy of 94.9%, which is more suitable for processing radar point clouds than the popular deep learning frameworks and shows promising performance. Full article