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Radar Remote Sensing for Applications in Intelligent Transportation

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Urban Remote Sensing".

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 27871

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Special Issue Editors

Dr. Zhihuo Xu

E-Mail Website
Guest Editor

School of Transportation, Nantong University, Nantong 226019, China
Interests: automotive radar system design and signal processing; synthetic aperture radar (SAR); MIMO radar; cognitive radar; radar image processing; radar targets tracking

Dr. Jianping Wang

E-Mail Website
Guest Editor

Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS), Delft University of Technology, 2628 CD Delft, The Netherlands
Interests: microwave imaging; signal processing; antenna array design

Prof. Dr. Yongwei Zhang

E-Mail Website
Guest Editor

School of Transportation, Nantong University, Nantong 226019, China
Interests: array antenna design based on flexible materials; electromagnetics; antenna array calibration; radio astronomy instrumentation

Special Issue Information

Dear Colleagues,

The rise of driverless vehicles has raised higher demands and challenges for automotive radar. In the past five years, the millimeter wave radar has broken through the large-scale virtual array technology, so that the angle resolution of the radar can reach 1 degree, and the acquisition of range, velocity, azimuth, and elevation information emerged, i.e., 4D radar. If the magnitude information of the target is considered, there is a 5D radar. It also breaks through the antenna on-chip packaging technology, which makes the radar PCB circuit design simpler and the time to market is greatly reduced. The next 10 years radar will move the working frequency to low Terahertz channel, and extend promising applications in intelligent transportation, such as higher-level autonomous vehicle, intelligent traffic flow detection, intelligent traffic signal design, and fusion application with vehicular network, which will become the milestone event of civil radar for intelligent transportation application, and greatly improve the traffic mobility and the traffic safety.

This special issue aims to provide the latest state-of-the-art methods of radar remote sensing and its applications in intelligent transportation. The main content covers the current technical bottlenecks of intelligent transportation radar, such as multipath interference of radar, mutual interference between radars, as well as the latest radar technologies, such as low Terahertz automotive radar, and applications in intelligent transportation, etc.

Multipath interference of automotive radar.
Mutual Interference between automotive radars.
Low terahertz automotive radar systems and signal processing.
Array antenna design and its application in intelligent transportation.
Range, Doppler, and spatial cell migration of 4D and 5D radars.
Radar-based multi-target tracking.
Radar-based intelligent traffic flow detection and traffic classification.
Radar-based intelligent traffic signal design and application.
When intelligent traffic radar meets vehicle to everything network.
Micro-Doppler information detection for intelligent traffic and applications.
mm-Wave radar-based in-cabin sensing.

Dr. Zhihuo Xu
Dr. Jianping Wang
Prof. Dr. Yongwei Zhang
Guest Editors

Manuscript Submission Information

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Keywords

Intelligent transportation
Automotive radar
Interference suppression
4D and 5D radars.
Low terahertz automotive radar
Radar antenna
Radar and vehicle to everything(V2X)

Published Papers (11 papers)

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Research

20 pages, 5362 KiB

Open AccessArticle

Tracking of Multiple Static and Dynamic Targets for 4D Automotive Millimeter-Wave Radar Point Cloud in Urban Environments

by Bin Tan, Zhixiong Ma, Xichan Zhu, Sen Li, Lianqing Zheng, Libo Huang and Jie Bai

Remote Sens. 2023, 15(11), 2923; https://doi.org/10.3390/rs15112923 - 3 Jun 2023

Cited by 2 | Viewed by 2252

Abstract

This paper presents a target tracking algorithm based on 4D millimeter-wave radar point cloud information for autonomous driving applications, which addresses the limitations of traditional 2 + 1D radar systems by using higher resolution target point cloud information that enables more accurate motion state estimation and target contour information. The proposed algorithm includes several steps, starting with the estimation of the ego vehicle’s velocity information using the radial velocity information of the millimeter-wave radar point cloud. Different clustering suggestions are then obtained using a density-based clustering method, and correlation regions of the targets are obtained based on these clustering suggestions. The binary Bayesian filtering method is then used to determine whether the targets are dynamic or static targets based on their distribution characteristics. For dynamic targets, Kalman filtering is used to estimate and update the state of the target using trajectory and velocity information, while for static targets, the rolling ball method is used to estimate and update the shape contour boundary of the target. Unassociated measurements are estimated for the contour and initialized for the trajectory, and unassociated trajectory targets are selectively retained and deleted. The effectiveness of the proposed method is verified using real data. Overall, the proposed target tracking algorithm based on 4D millimeter-wave radar point cloud information has the potential to improve the accuracy and reliability of target tracking in autonomous driving applications, providing more comprehensive motion state and target contour information for better decision making. Full article

(This article belongs to the Special Issue Radar Remote Sensing for Applications in Intelligent Transportation)

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24 pages, 14326 KiB

Open AccessArticle

Coherent-on-Receive Synthesis Using Dominant Scatterer in Millimeter-Wave Distributed Coherent Aperture Radar

by Can Liang, Yang Li, Xueyao Hu, Yanhua Wang, Liang Zhang, Min Wang and Junliang Guo

Remote Sens. 2023, 15(6), 1505; https://doi.org/10.3390/rs15061505 - 8 Mar 2023

Cited by 2 | Viewed by 1355

Abstract

The target signal-to-noise ratio (SNR) can be notably improved by coherent-on-receive synthesis (CoRS) in distributed coherent aperture radar (DCAR). A core challenge of CoRS is to estimate the coherent parameters (CPs), including time, frequency, and phase, in order to cohere the multi-radar within DCAR. Conventional methods usually rely on the target’s own information to estimate the CPs, which is not available in highly dynamic environments. Additionally, the CPs of different targets, especially the phase, are unequal in high-frequency systems. This means that we cannot directly use the CPs of one target to compensate for others. To address these issues, an adaptive CoRS method using the dominant scatterer is proposed for millimeter-wave (MMW) DCAR in this paper. The basic idea is to correct the CPs of the dominant scatterer to compensate for other targets. The novelty lies in the adaptive phase compensation based on the estimated CPs. This phase compensation depends on a series of discrete phase values, which are derived from the limit of synthesis loss within a given configuration. Hence, this method avoids the requirement of prior information or massive searches for the possible locations of other targets. Moreover, the dominant scatterer in this work is an unknown target with strong scattering points in radar detection scenarios, and we focus on analyzing its selection criteria. To validate the proposed method, a prototype system has been fabricated and evaluated through experiments. It is demonstrated that the multi-target can realize CoRS effectively, thus enhancing the target SNR. Full article

(This article belongs to the Special Issue Radar Remote Sensing for Applications in Intelligent Transportation)

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20 pages, 4151 KiB

Open AccessArticle

Joint Radar and Communications Waveform Design Based on Complementary Sequence Sets

by Haichuan Li, Yongjun Liu, Guisheng Liao and Yufeng Chen

Remote Sens. 2023, 15(3), 645; https://doi.org/10.3390/rs15030645 - 21 Jan 2023

Cited by 4 | Viewed by 1715

Abstract

The joint radar and communications (JRC) waveform often has a high range sidelobe, which will degrade the target detection performance of an automotive JRC system. To solve this problem, a joint radar and communications complementary waveform group (JRC-CWG) design method is proposed in this paper by exploiting the philosophy of the complementary sequence. In the JRC-CWG, the traditional unimodular communications waveforms, such as the binary phase shift keying (BPSK) waveform, are used to perform the communications function. The sum of the autocorrelation function (SACF) of JRC-CWG is optimized to minimize the sidelobe level. Furthermore, considering that the JRC-CWG has poor Doppler resilience, a Doppler-resilient joint radar and communications complementary waveform (DR-JRC-CWG) design method is proposed to improve the Doppler resilience. Finally, the simulation results show that the proposed JRC-CWG and DR-JRC-CWG have superior radar performances without the degradation in communications performance in terms of the bit error rate (BER). Full article

(This article belongs to the Special Issue Radar Remote Sensing for Applications in Intelligent Transportation)

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18 pages, 1110 KiB

Open AccessArticle

Reweighted Robust Particle Filtering Approach for Target Tracking in Automotive Radar Application

by Qisong Wu, Lingjie Chen, Yanping Li, Zijun Wang, Shuai Yao and Hao Li

Remote Sens. 2022, 14(21), 5477; https://doi.org/10.3390/rs14215477 - 31 Oct 2022

Cited by 1 | Viewed by 1026

Abstract

In view of the decline of filtering accuracy caused by measured outliers in target tracking application, a novel reweighted robust particle filter is proposed to acquire accurate state estimates in an automotive radar system. To infer the importance of each entry in the multidimensional contaminated measurement vector, we employ a weight vector, which follows a Gamma distribution, to model the measured noise and carry out accurate state estimates. Additionally, the particle filter method is employed to perform approximate posterior inference of state estimates in the nonlinear model. The Cramer–Rao lower bound is provided for the performance evaluation of the proposed method. Both simulation and experimental results demonstrate the superiorities of the proposed algorithm over other robust solutions. Full article

(This article belongs to the Special Issue Radar Remote Sensing for Applications in Intelligent Transportation)

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23 pages, 1862 KiB

Open AccessArticle

Radar and Communication Spectral Coexistence on Moving Platform with Interference Suppression

by Junhui Qian, Ziyu Liu, Yuanyuan Lu, Le Zheng, Ailing Zhang and Fengxia Han

Remote Sens. 2022, 14(19), 5018; https://doi.org/10.3390/rs14195018 - 9 Oct 2022

Cited by 1 | Viewed by 1469

Abstract

With the development of intelligent transportation, radar and communication on moving platforms are competing for the spectrum. In this paper, we propose and demonstrate a new algorithmic framework for radar-communication spectral coexistence system on moving platform with mutual interference suppression, in which communication rate and the radar signal-to-interference-plus-noise ratio (SINR) are simultaneously optimized, under the energy constraints for the two systems and the radar constant modulus constraint. The radar spatial-temporal filter at the receiver and transmitting waveform are optimized, while the codebook matrix is optimized for the communication system. To cope with the established non-convex problem with triplet variables, we decouple the original problem into multiple subproblems, for which an alternating algorithm based on iterative procedures is derived with lower computational complexity. Specifically, the subproblems of communication codebook and radar filter design are convex and the closed-form solutions can be easily obtained, while the radar waveform optimization is non-convex. Then we propose a novel scheme by exploiting the alternating direction method of multipliers (ADMM) based on minorization-maximization (MM) framework. Finally, to reveal the effectiveness of the proposed algorithm in different scenarios, numerical results are provided. Full article

(This article belongs to the Special Issue Radar Remote Sensing for Applications in Intelligent Transportation)

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23 pages, 21463 KiB

Open AccessArticle

Incoherent Interference Detection and Mitigation for Millimeter-Wave FMCW Radars

by Zhihuo Xu, Shuaikang Xue and Yuexia Wang

Remote Sens. 2022, 14(19), 4817; https://doi.org/10.3390/rs14194817 - 27 Sep 2022

Cited by 8 | Viewed by 2405

Abstract

Current automotive radar technology is almost exclusively implemented using frequency modulated continuous wave (FMCW) radar in the millimeter wave bands. Unfortunately, incoherent interference is becoming a serious problem due to the increasing number of automotive radars in dense traffic situations. To address this issue, this article presents a sparsity-based technique for mitigating the incoherent interference between FMCW radars. First, a low-pass filter-based technique is developed to detect the envelope of the interference. Next, the labeled regions where interference is present are considered as missing data. In this way, the problem of mitigating interference is further formulated as the restoration of the echo using L1 norm-regularized least squares. Finally, the alternating direction method of the multipliers-based technique is applied to restore the radar echoes. Extensive experimental results demonstrate the effective performance of the proposed approach. Compared to state-of-the-art interference mitigation methods, the proposed method remarkably improves the quality of radar targets. Full article

(This article belongs to the Special Issue Radar Remote Sensing for Applications in Intelligent Transportation)

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15 pages, 8172 KiB

Open AccessCommunication

Low Sidelobe Series-Fed Patch Planar Array with AMC Structure to Suppress Parasitic Radiation

by Qingquan Tan, Kuikui Fan, Wenwen Yang and Guoqing Luo

Remote Sens. 2022, 14(15), 3597; https://doi.org/10.3390/rs14153597 - 27 Jul 2022

Cited by 4 | Viewed by 2892

Abstract

For automobile radar systems, the antenna array requires a low sidelobe level (SLL) to reduce interference. A low-SLL and low-cost planar antenna array are proposed in this article for millimeter-wave automotive radar applications. The proposed array consists of six linear series-fed patch arrays, a series distribution network using a grounded co-planar waveguide (GCPW), and a bed of nails. First, a hybrid HFSS-MATLAB optimization platform is set up to easily obtain good impedance matching and low SLL of the linear series-fed patch array. Then, a six-way GCPW power divider is designed to combine the optimized linear sub-array to achieve a planar array. However, since CCPW is a semi-open structure, like a microstrip line, the parasitic radiation generated by the GCPW feeding network will lead to the deterioration of the SLL. To solve this problem, a bed of nails—as an artificial magnetic conductor (AMC)—is designed and placed above the feeding networking to create an electromagnetic stopband in the working band. Its working mechanism has been explained in detail. The feeding network cannot effectively radiate electromagnetic waves into free space. Thus, the parasitic radiation can be suppressed. A low-SLL planar array prototype working at 79 GHz is designed, manufactured, and measured. The measured results confirm that the proposed low-SLL planar array has a −10 dB impedance bandwidth of 3 GHz from 77 to 80 GHz and a maximum peak gain of 21 dBi. The measured SLL is −24 dB and −23 dB in the E-plane and H-plane at 79 GHz, respectively. The proposed low SLL array can be used for adaptive cruise control (ACC) system applications. Full article

(This article belongs to the Special Issue Radar Remote Sensing for Applications in Intelligent Transportation)

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34 pages, 12477 KiB

Open AccessArticle

HTC+ for SAR Ship Instance Segmentation

by Tianwen Zhang and Xiaoling Zhang

Remote Sens. 2022, 14(10), 2395; https://doi.org/10.3390/rs14102395 - 17 May 2022

Cited by 41 | Viewed by 2744

Abstract

Existing instance segmentation models mostly pay less attention to the targeted characteristics of ships in synthetic aperture radar (SAR) images, which hinders further accuracy improvements, leading to poor segmentation performance in more complex SAR image scenes. To solve this problem, we propose a hybrid task cascade plus (HTC+) for better SAR ship instance segmentation. Aiming at the specific SAR ship task, seven techniques are proposed to ensure the excellent performance of HTC+ in more complex SAR image scenes, i.e., a multi-resolution feature extraction network (MRFEN), an enhanced feature pyramid net-work (EFPN), a semantic-guided anchor adaptive learning network (SGAALN), a context ROI extractor (CROIE), an enhanced mask interaction network (EMIN), a post-processing technique (PPT), and a hard sample mining training strategy (HSMTS). Results show that each of them offers an observable accuracy gain, and the instance segmentation performance in more complex SAR image scenes becomes better. On two public datasets SSDD and HRSID, HTC+ surpasses the other nine competitive models. It achieves 6.7% higher box AP and 5.0% higher mask AP than HTC on SSDD. These are 4.9% and 3.9% on HRSID. Full article

(This article belongs to the Special Issue Radar Remote Sensing for Applications in Intelligent Transportation)

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17 pages, 1269 KiB

Open AccessArticle

Multi-Hand Gesture Recognition Using Automotive FMCW Radar Sensor

by Yong Wang, Di Wang, Yunhai Fu, Dengke Yao, Liangbo Xie and Mu Zhou

Remote Sens. 2022, 14(10), 2374; https://doi.org/10.3390/rs14102374 - 14 May 2022

Cited by 9 | Viewed by 2673

Abstract

With the development of human–computer interaction(s) (HCI), hand gestures are playing increasingly important roles in our daily lives. With hand gesture recognition (HGR), users can play virtual games together, control the smart equipment, etc. As a result, this paper presents a multi-hand gesture recognition system using automotive frequency modulated continuous wave (FMCW) radar. Specifically, we first constructed the range-Doppler map (RDM) and range-angle map (RAM), and then suppressed the spectral leakage, and dynamic and static interferences. Since the received echo signals with multi-hand gestures are mixed together, we propose a spatiotemporal path selection algorithm to separate the mixed multi-hand gestures. A dual 3D convolutional neural network-based feature fusion network is proposed for feature extraction and classification. We developed the FMCW radar-based platform to evaluate the performance of the proposed multi-hand gesture recognition method; the experimental results show that the proposed method can achieve an average recognition accuracy of 93.12% when eight gestures with two hands are performed simultaneously. Full article

(This article belongs to the Special Issue Radar Remote Sensing for Applications in Intelligent Transportation)

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23 pages, 2557 KiB

Open AccessArticle

A CFAR Algorithm Based on Monte Carlo Method for Millimeter-Wave Radar Road Traffic Target Detection

by Bo Yang and Hua Zhang

Remote Sens. 2022, 14(8), 1779; https://doi.org/10.3390/rs14081779 - 7 Apr 2022

Cited by 13 | Viewed by 5060

Abstract

The development of Intelligent Transportation Systems (ITS) puts forward higher requirements for millimeter-wave radar surveillance in the traffic environment, such as lower time delay, higher sensitivity, and better multi-target detection capability. The Constant False Alarm Rate (CFAR) detector plays a vital role in the adaptive target detection of the radar. Still, traditional CFAR detection algorithms use a sliding window to find the target limit radar detection speed and efficiency. In such cases, we propose and discuss a CFAR detection method, which transforms the Monte Carlo simulation principle into randomly sampling instantaneous Range–Doppler Matrix (RDM) data, to improve the detection ability of radar for moving targets such as pedestrians and vehicles in the traffic environment. Compared with conventional methods, simulation and real experiments show that the method breaks through the reference window limitation and has higher detection sensitivity, higher detection accuracy, and lower detection delay. We hope to promote the detection application of millimeter-wave radar in road traffic scenes. Full article

(This article belongs to the Special Issue Radar Remote Sensing for Applications in Intelligent Transportation)

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19 pages, 552 KiB

Open AccessArticle

Cramér-Rao Bound of Joint DOA-Range Estimation for Coprime Frequency Diverse Arrays

by Zihuan Mao, Shengheng Liu, Si Qin and Yongming Huang

Remote Sens. 2022, 14(3), 583; https://doi.org/10.3390/rs14030583 - 26 Jan 2022

Cited by 9 | Viewed by 2122

Abstract

Frequency diverse array (FDA) produces a beampattern with controllable direction and range by slightly shifting the carrier frequencies across the elements, which is attractive in many applications. By further incorporating coprime array structure and coprime frequency offsets, improved degrees-of-freedom and spatial/range resolutions have been achieved. For such a relatively new array configuration, theoretical performance analyses are essential to explore the potentials and to facilitate practical implementation. In this work, we consider coprime-FDA-based joint/separate angle-range estimation of far-field targets that exhibit two different types of Swerling fluctuation behavior, which are respectively modelled as deterministic and stochastic sources. Analytical expressions of the Cramér–Rao bounds (CRB) and numerical simulations for both cases are provided. The results reveal that the relationship between CRB and coprime FDA parameters is not simply monotonic. As shown in the numerical simulations, the CRB of coprime FDA outperforms that of uniform FDA-MIMO for more than

60 %

under commonly-adopted coprime patterns. The presented results can be used as a guideline for optimal design of coprime FDA. Full article

(This article belongs to the Special Issue Radar Remote Sensing for Applications in Intelligent Transportation)

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