Millimeter-wave (mmWave) radar has been widely applied in target detection. However, due to multipath and occlusion, radar often detects ghosts, especially in indoor environments. Existing solutions are mostly tailored to specific, simplified scenarios. To identify radar ghosts in diverse and complex indoor environments, we propose a data-driven approach. A thoughtful indoor radar ghost dataset is created with a multimodal data acquisition and automatic annotation system. And PairwiseNet, an end-to-end deep neural network adept at handling point-pair relationships within sparse point clouds, is proposed for radar ghost recognition. Multiframe accumulation is also implemented in PairwiseNet. To further enhance PairwiseNet, an additional network incorporating grid maps and U-Net is developed for constructing environmental maps from sequential point clouds. This network is trained through cross-modal distillation, with a depth camera as the teacher. Finally, a series of experiments validates the effectiveness of the proposed method in identifying indoor radar ghosts and autonomously constructing environmental maps. The classification accuracy on the test set reaches 96.0%, accurately identifying ghosts in the vast majority of cases.
{"title":"A Data-Driven Method for Indoor Radar Ghost Recognition With Environmental Mapping","authors":"Ruizhi Liu;Xinghui Song;Jiawei Qian;Shuai Hao;Yue Lin;Hongtao Xu","doi":"10.1109/TRS.2024.3456891","DOIUrl":"https://doi.org/10.1109/TRS.2024.3456891","url":null,"abstract":"Millimeter-wave (mmWave) radar has been widely applied in target detection. However, due to multipath and occlusion, radar often detects ghosts, especially in indoor environments. Existing solutions are mostly tailored to specific, simplified scenarios. To identify radar ghosts in diverse and complex indoor environments, we propose a data-driven approach. A thoughtful indoor radar ghost dataset is created with a multimodal data acquisition and automatic annotation system. And PairwiseNet, an end-to-end deep neural network adept at handling point-pair relationships within sparse point clouds, is proposed for radar ghost recognition. Multiframe accumulation is also implemented in PairwiseNet. To further enhance PairwiseNet, an additional network incorporating grid maps and U-Net is developed for constructing environmental maps from sequential point clouds. This network is trained through cross-modal distillation, with a depth camera as the teacher. Finally, a series of experiments validates the effectiveness of the proposed method in identifying indoor radar ghosts and autonomously constructing environmental maps. The classification accuracy on the test set reaches 96.0%, accurately identifying ghosts in the vast majority of cases.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"2 ","pages":"910-923"},"PeriodicalIF":0.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142324339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-03DOI: 10.1109/TRS.2024.3453708
Jonathan Bott;Muhammed Ali Yildirim;Benedikt Sievert;Florian Vogelsang;Tobias Welling;Philipp Konze;Daniel Erni;Andreas Rennings;Nils Pohl
This article introduces a 240-GHz multiple-input-multiple-output (MIMO) radar chipset, consisting of a 120-GHz voltage-controlled oscillator (VCO) monolithic microwave integrated circuit (MMIC) for generating the local oscillator (LO) signal and a 240-GHz transceiver (TRX) MMIC, doubling the frequency and containing one transmitter (Tx) and one receiver (Rx) channel. The Tx channel has a digital vector modulator (VM), allowing for phase adjustments. The 120-GHz VCO has a tuning range of 27.2 GHz (23.6%). The MIMO frequency-modulated continuous-wave (FMCW) system capabilities are demonstrated using a phase-locked loop (PLL)-based VCO stabilization generating wideband, 30-GHz FMCW chirps, which are radiated using a time-division multiplexing (TDM) technique. The MMICs feature a cascadable approach, enabling the scalability of the array size by placing multiple TRX MMICs close to each other using a daisy chain approach. Furthermore, a circular polarized on-chip antenna allows rotation of the MMICs, and the TRX MMIC can be connected to two adjacent edges of the VCO MMIC, creating a 2D array for detecting targets in 3-D space. In the demonstrator setup using eight MMICs, the eight Tx channels of the MMICs generate an equivalent isotropically radiated power (EIRP) of 0 dBm each, reflected from the target and received by eight Rx channels. Overall, the demonstrator system contains 64 virtual elements integrated on an array size of less than $10 times 10~text {mm}^{2}$ .
{"title":"An 8 × 8 MIMO Radar System Utilizing Cascadable Transceiver MMICs With On-Chip Antennas at 240 GHz","authors":"Jonathan Bott;Muhammed Ali Yildirim;Benedikt Sievert;Florian Vogelsang;Tobias Welling;Philipp Konze;Daniel Erni;Andreas Rennings;Nils Pohl","doi":"10.1109/TRS.2024.3453708","DOIUrl":"https://doi.org/10.1109/TRS.2024.3453708","url":null,"abstract":"This article introduces a 240-GHz multiple-input-multiple-output (MIMO) radar chipset, consisting of a 120-GHz voltage-controlled oscillator (VCO) monolithic microwave integrated circuit (MMIC) for generating the local oscillator (LO) signal and a 240-GHz transceiver (TRX) MMIC, doubling the frequency and containing one transmitter (Tx) and one receiver (Rx) channel. The Tx channel has a digital vector modulator (VM), allowing for phase adjustments. The 120-GHz VCO has a tuning range of 27.2 GHz (23.6%). The MIMO frequency-modulated continuous-wave (FMCW) system capabilities are demonstrated using a phase-locked loop (PLL)-based VCO stabilization generating wideband, 30-GHz FMCW chirps, which are radiated using a time-division multiplexing (TDM) technique. The MMICs feature a cascadable approach, enabling the scalability of the array size by placing multiple TRX MMICs close to each other using a daisy chain approach. Furthermore, a circular polarized on-chip antenna allows rotation of the MMICs, and the TRX MMIC can be connected to two adjacent edges of the VCO MMIC, creating a 2D array for detecting targets in 3-D space. In the demonstrator setup using eight MMICs, the eight Tx channels of the MMICs generate an equivalent isotropically radiated power (EIRP) of 0 dBm each, reflected from the target and received by eight Rx channels. Overall, the demonstrator system contains 64 virtual elements integrated on an array size of less than \u0000<inline-formula> <tex-math>$10 times 10~text {mm}^{2}$ </tex-math></inline-formula>\u0000.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"2 ","pages":"805-820"},"PeriodicalIF":0.0,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10663766","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142246510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In recent years, the research community has gained more interest in spectral cooperation between radar and communication systems. This article introduces a communication-aided radar measurement model as a function of transmitted waveforms in a cooperative radar-communication system (CRCS). For this investigation, a linear frequency-modulated (LFM) pulse radar waveform, a nonlinear frequency-modulated pulse radar waveform, and a quadrature amplitude-modulated (QAM) communication waveform are considered, and the target state estimation performance is analyzed. At a given epoch, the target’s position is estimated by considering the range and the range rate as measurements in an iterative least-squares (ILS) framework. After that, the Kalman filter (KF) is used to estimate the target dynamics using converted measurements. In addition, the error in the estimated position of the target is quantified with the root-mean-square error (RMSE) and the posterior Cramér-Rao lower bound (PCRLB). Eventually, the simulated results convey that the combination of the nonlinear frequency modulation (NLFM) radar waveform and the QAM communication waveform is more suitable for the estimation of the target state than the other combination (LFM radar waveform and QAM communication waveform).
{"title":"Communication-Aided Target State Estimation in a Cooperative Radar-Communication System","authors":"Mahipathi Ashoka Chakravarthi;Bethi Pardhasaradhi;Pathipati Srihari;John D’Souza;Paramananda Jena;Jing Zhou;Linga Reddy Cenkeramaddi","doi":"10.1109/TRS.2024.3452869","DOIUrl":"https://doi.org/10.1109/TRS.2024.3452869","url":null,"abstract":"In recent years, the research community has gained more interest in spectral cooperation between radar and communication systems. This article introduces a communication-aided radar measurement model as a function of transmitted waveforms in a cooperative radar-communication system (CRCS). For this investigation, a linear frequency-modulated (LFM) pulse radar waveform, a nonlinear frequency-modulated pulse radar waveform, and a quadrature amplitude-modulated (QAM) communication waveform are considered, and the target state estimation performance is analyzed. At a given epoch, the target’s position is estimated by considering the range and the range rate as measurements in an iterative least-squares (ILS) framework. After that, the Kalman filter (KF) is used to estimate the target dynamics using converted measurements. In addition, the error in the estimated position of the target is quantified with the root-mean-square error (RMSE) and the posterior Cramér-Rao lower bound (PCRLB). Eventually, the simulated results convey that the combination of the nonlinear frequency modulation (NLFM) radar waveform and the QAM communication waveform is more suitable for the estimation of the target state than the other combination (LFM radar waveform and QAM communication waveform).","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"2 ","pages":"832-848"},"PeriodicalIF":0.0,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142274950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-02DOI: 10.1109/TRS.2024.3452764
Pucheng Li;Zegang Ding;Linghao Li;Linhan Lv;Zhe Li;Rui Zhu;Guanxing Wang
The distributed coherent aperture radar (DCAR) utilizes full coherent processing (FCP). Compared to single radar unit observations, N radar units can achieve an $N^{3}$ times increase in signal-to-noise ratio (SNR), providing an advantage in observing distant targets. However, its stringent requirements for time and phase of multiple radar units make coherent parameters (CPs) estimation the crucial aspect of FCP. This article introduces a satellite data-driven CPs estimation method via hierarchical and hybrid generalized Radon-Fourier transform (HHGRFT). First, the FCP procedure is outlined, and the signal model with CPs is established. Second, the principles for selecting satellites and observation time in the experimental setup are induced, along with an analysis of the SNR variations during processing. Furthermore, through a hierarchical processing approach using the generalized Radon-Fourier transform (GRFT), interpulse coherence (IPC) processing, interunit-radar coherence (IURC) processing, and inter-subaperture coherent or noncoherent processing are sequentially conducted. The utilization of generalized sharpness (GS) and gradient descent method facilitated CPs estimation, subsequently enhancing the SNR post-coherence processing. Finally, the proposed method has been validated through simulation and successfully applied to a real DCAR system.
{"title":"A Satellite Data-Driven Coherent Parameters Estimation Method via Hierarchical HGRFT for Distributed Coherent Aperture Radar","authors":"Pucheng Li;Zegang Ding;Linghao Li;Linhan Lv;Zhe Li;Rui Zhu;Guanxing Wang","doi":"10.1109/TRS.2024.3452764","DOIUrl":"https://doi.org/10.1109/TRS.2024.3452764","url":null,"abstract":"The distributed coherent aperture radar (DCAR) utilizes full coherent processing (FCP). Compared to single radar unit observations, N radar units can achieve an \u0000<inline-formula> <tex-math>$N^{3}$ </tex-math></inline-formula>\u0000 times increase in signal-to-noise ratio (SNR), providing an advantage in observing distant targets. However, its stringent requirements for time and phase of multiple radar units make coherent parameters (CPs) estimation the crucial aspect of FCP. This article introduces a satellite data-driven CPs estimation method via hierarchical and hybrid generalized Radon-Fourier transform (HHGRFT). First, the FCP procedure is outlined, and the signal model with CPs is established. Second, the principles for selecting satellites and observation time in the experimental setup are induced, along with an analysis of the SNR variations during processing. Furthermore, through a hierarchical processing approach using the generalized Radon-Fourier transform (GRFT), interpulse coherence (IPC) processing, interunit-radar coherence (IURC) processing, and inter-subaperture coherent or noncoherent processing are sequentially conducted. The utilization of generalized sharpness (GS) and gradient descent method facilitated CPs estimation, subsequently enhancing the SNR post-coherence processing. Finally, the proposed method has been validated through simulation and successfully applied to a real DCAR system.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"2 ","pages":"791-804"},"PeriodicalIF":0.0,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142246524","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-02DOI: 10.1109/TRS.2024.3452868
Seonghyeon Kang;Kawon Han;Songcheol Hong
This article presents a method to estimate nonlinear distortion of an orthogonal frequency-division multiplexing (OFDM) radar signal by using detected target parameters. Since high transmitting power is desirable for OFDM radar to have a long detection range, the transmitter (TX) is preferred to work in a nonlinear region for high power efficiency. This causes strong distortions of the OFDM radar signals, which have a high peak-to-average power ratio (PAPR). Conventionally, this distortion can be compensated by utilizing equalization at the receiver (RX) or digital predistortion (DPD) at the TX. However, both approaches require information on the transmitted signals obtained through an additional feedback path, which increases the hardware complexity of the radar system. To address this issue, a sensing-aided distortion estimation (SADE) is proposed to estimate the distorted OFDM signals. Initially, radar processing is performed on the received signals with the prior known undistorted symbols. This allows detection of some initial targets in the range-Doppler (RD) domain. Once the target parameters are detected, the distorted symbols can be estimated through division of the received signals by the calculated target signals. This approach leverages the initial target sensing as a feedback loop between the TX and RX. This allows estimation of the distorted OFDM symbols without any additional hardware. The radar processing for subsequent targets demodulates the received signals by using the estimated distorted symbols.
{"title":"Sensing-Aided Distortion Estimation for OFDM Radar With Nonlinear Transmitter","authors":"Seonghyeon Kang;Kawon Han;Songcheol Hong","doi":"10.1109/TRS.2024.3452868","DOIUrl":"https://doi.org/10.1109/TRS.2024.3452868","url":null,"abstract":"This article presents a method to estimate nonlinear distortion of an orthogonal frequency-division multiplexing (OFDM) radar signal by using detected target parameters. Since high transmitting power is desirable for OFDM radar to have a long detection range, the transmitter (TX) is preferred to work in a nonlinear region for high power efficiency. This causes strong distortions of the OFDM radar signals, which have a high peak-to-average power ratio (PAPR). Conventionally, this distortion can be compensated by utilizing equalization at the receiver (RX) or digital predistortion (DPD) at the TX. However, both approaches require information on the transmitted signals obtained through an additional feedback path, which increases the hardware complexity of the radar system. To address this issue, a sensing-aided distortion estimation (SADE) is proposed to estimate the distorted OFDM signals. Initially, radar processing is performed on the received signals with the prior known undistorted symbols. This allows detection of some initial targets in the range-Doppler (RD) domain. Once the target parameters are detected, the distorted symbols can be estimated through division of the received signals by the calculated target signals. This approach leverages the initial target sensing as a feedback loop between the TX and RX. This allows estimation of the distorted OFDM symbols without any additional hardware. The radar processing for subsequent targets demodulates the received signals by using the estimated distorted symbols.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"2 ","pages":"821-831"},"PeriodicalIF":0.0,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142246511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-26DOI: 10.1109/TRS.2024.3449347
Zhennan Liang;Zihan Yan;Meng Gao;Shaoqiang Chang;Quanhua Liu
An important challenge in group target tracking is the separation of individual targets within the group. Group target separation can lead to significant fluctuations in the position of the group center and the shape of the group, leading to decreased tracking accuracy and potential target loss. Moreover, when the target group consists of multiple objects with uncertain spatial positions, especially during separation, rapidly reconstructing the shape of the group target becomes challenging. This article proposes a novel method for detecting separation events and stable tracking to address related issues. Initially, we establish rapid ellipsoidal modeling of the group target shape through measurement mapping. Subsequently, group target separation events are predicted in real time by monitoring changes in ellipsoid volume between frames. Meanwhile, an adaptive association gate and a group clustering threshold are set to assist in separation assessment. In addition, utilize the pre-separation group target state to stabilize subgroups’ tracking after separation. The simulation results demonstrate that the proposed algorithm effectively and timely detects group target separation and enhances the performance of tracking separated group targets.
{"title":"Three-Dimensional Group Target Separation Detection Method Based on Ellipsoid Shape Reconstruction","authors":"Zhennan Liang;Zihan Yan;Meng Gao;Shaoqiang Chang;Quanhua Liu","doi":"10.1109/TRS.2024.3449347","DOIUrl":"https://doi.org/10.1109/TRS.2024.3449347","url":null,"abstract":"An important challenge in group target tracking is the separation of individual targets within the group. Group target separation can lead to significant fluctuations in the position of the group center and the shape of the group, leading to decreased tracking accuracy and potential target loss. Moreover, when the target group consists of multiple objects with uncertain spatial positions, especially during separation, rapidly reconstructing the shape of the group target becomes challenging. This article proposes a novel method for detecting separation events and stable tracking to address related issues. Initially, we establish rapid ellipsoidal modeling of the group target shape through measurement mapping. Subsequently, group target separation events are predicted in real time by monitoring changes in ellipsoid volume between frames. Meanwhile, an adaptive association gate and a group clustering threshold are set to assist in separation assessment. In addition, utilize the pre-separation group target state to stabilize subgroups’ tracking after separation. The simulation results demonstrate that the proposed algorithm effectively and timely detects group target separation and enhances the performance of tracking separated group targets.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"2 ","pages":"767-777"},"PeriodicalIF":0.0,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142165052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-26DOI: 10.1109/TRS.2024.3449333
Lukas Sigg;Lucas Giroto de Oliveira;Zsolt Kollár;Jan Schöpfel;Tobias T. Braun;Nils Pohl;Thomas Zwick;Benjamin Nuss
Radar networks can offer superior performance compared to individual sensors. However, synchronization is crucial for realizing such a radar network coherently. Digital systems, in particular, provide new opportunities for over-the-air synchronization via signal processing. To synchronize the nodes of a digital radar network, correction of the carrier frequency offset (CFO), sampling frequency offset (SFO), and timing offset (TO) is necessary. A coarse synchronization can be achieved, for example, afterward through low-frequency (LF) coupling of the individual sensors, with fine synchronization realized through signal processing. For fine synchronization, either a target or coupling between the two radar sensors with sufficient signal-to-noise ratio (SNR) is required. The limits of this synchronization approach are primarily defined by the range and Doppler shift ambiguities of the individual sensors. In this article, simulations and measurements demonstrate the feasibility of such a system.
{"title":"Over-the-Air Synchronization for Coherent Digital Automotive Radar Networks","authors":"Lukas Sigg;Lucas Giroto de Oliveira;Zsolt Kollár;Jan Schöpfel;Tobias T. Braun;Nils Pohl;Thomas Zwick;Benjamin Nuss","doi":"10.1109/TRS.2024.3449333","DOIUrl":"https://doi.org/10.1109/TRS.2024.3449333","url":null,"abstract":"Radar networks can offer superior performance compared to individual sensors. However, synchronization is crucial for realizing such a radar network coherently. Digital systems, in particular, provide new opportunities for over-the-air synchronization via signal processing. To synchronize the nodes of a digital radar network, correction of the carrier frequency offset (CFO), sampling frequency offset (SFO), and timing offset (TO) is necessary. A coarse synchronization can be achieved, for example, afterward through low-frequency (LF) coupling of the individual sensors, with fine synchronization realized through signal processing. For fine synchronization, either a target or coupling between the two radar sensors with sufficient signal-to-noise ratio (SNR) is required. The limits of this synchronization approach are primarily defined by the range and Doppler shift ambiguities of the individual sensors. In this article, simulations and measurements demonstrate the feasibility of such a system.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"2 ","pages":"739-751"},"PeriodicalIF":0.0,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142152102","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-16DOI: 10.1109/TRS.2024.3444785
Yoon-SL Kim;David Schvartzman;Robert D. Palmer;Tian-You Yu;Feng Nai;Christopher D. Curtis
Polarimetric weather radars, such as the Weather Surveillance Radar-1988 Doppler (WSR-88D), improve weather forecasts and provide valuable data for operational and scientific applications. The polarimetric capability adds additional insight into storm microphysics and greatly improves precipitation estimates. Nevertheless, fast-evolving weather events require high-temporal resolution data, which conventional radar systems (mechanical and dish-based) cannot provide. Phased array radar (PAR) offers superior observation capabilities with electronic beam steering and enhanced scanning agility. Furthermore, digital PAR enables 1-D (space and time) processing and overcomes limitations in clutter mitigation compared with the traditional radar systems that only use Doppler processing. Doppler processing is traditionally used to effectively filtering out ground clutter with zero mean velocity. In contrast, space-time processing (STP) enhances clutter mitigation to filter out both stationary and moving clutter through the joint spatial and temporal spectrum. This study aims to apply STP (nonadaptive) and space-time adaptive processing (STAP) to weather radar data and explore their benefits to improve Doppler velocity estimation of meteorological returns. Furthermore, the performance of STP and STAP for different digital PAR back ends, including fully digital and subarray systems, is investigated. Preliminary findings underscore the critical role of radar scanning parameters and environmental conditions, such as sample quantity, clutter-to-signal ratio (CSR), and signal-to-noise ratio (SNR), in the Doppler velocity estimation. Data collected with the recently completed Horus radar system are evaluated using STP and STAP. Results demonstrate the potential for improving data quality, particularly in Doppler velocity estimation within cluttered environments, through the application of STP and STAP techniques. The filtering algorithm with STAP demonstrates a substantial reduction in error within the Doppler velocity estimation, achieving approximately an eightfold improvement compared with the estimation derived from STP with filtering.
{"title":"Phased Array Weather Radar Architectures for Doppler Estimation With Space-Time Processing","authors":"Yoon-SL Kim;David Schvartzman;Robert D. Palmer;Tian-You Yu;Feng Nai;Christopher D. Curtis","doi":"10.1109/TRS.2024.3444785","DOIUrl":"https://doi.org/10.1109/TRS.2024.3444785","url":null,"abstract":"Polarimetric weather radars, such as the Weather Surveillance Radar-1988 Doppler (WSR-88D), improve weather forecasts and provide valuable data for operational and scientific applications. The polarimetric capability adds additional insight into storm microphysics and greatly improves precipitation estimates. Nevertheless, fast-evolving weather events require high-temporal resolution data, which conventional radar systems (mechanical and dish-based) cannot provide. Phased array radar (PAR) offers superior observation capabilities with electronic beam steering and enhanced scanning agility. Furthermore, digital PAR enables 1-D (space and time) processing and overcomes limitations in clutter mitigation compared with the traditional radar systems that only use Doppler processing. Doppler processing is traditionally used to effectively filtering out ground clutter with zero mean velocity. In contrast, space-time processing (STP) enhances clutter mitigation to filter out both stationary and moving clutter through the joint spatial and temporal spectrum. This study aims to apply STP (nonadaptive) and space-time adaptive processing (STAP) to weather radar data and explore their benefits to improve Doppler velocity estimation of meteorological returns. Furthermore, the performance of STP and STAP for different digital PAR back ends, including fully digital and subarray systems, is investigated. Preliminary findings underscore the critical role of radar scanning parameters and environmental conditions, such as sample quantity, clutter-to-signal ratio (CSR), and signal-to-noise ratio (SNR), in the Doppler velocity estimation. Data collected with the recently completed Horus radar system are evaluated using STP and STAP. Results demonstrate the potential for improving data quality, particularly in Doppler velocity estimation within cluttered environments, through the application of STP and STAP techniques. The filtering algorithm with STAP demonstrates a substantial reduction in error within the Doppler velocity estimation, achieving approximately an eightfold improvement compared with the estimation derived from STP with filtering.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"2 ","pages":"725-738"},"PeriodicalIF":0.0,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142117889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article presents a method for designing transmit beampattern in 4-D imaging automotive multiple-input-multiple-output (MIMO) radars, employing the distance between the designed and desired beampatterns as the design metric. Utilizing the $ell _{p}$ -norm criteria, we consider a broader range of p values, specifically for $p geq 2$ and $0 lt p leq 1$ , to enhance the optimization framework. The optimization problem formulated under these criteria is efficiently solved using the block successive upper bound minimization (BSUM) technique for discrete and continuous phase constraints. Our analysis verifies the convergence of the objective function and confirms the solution’s convergence, thereby establishing a new stopping criterion for this optimization process. Furthermore, we demonstrate that our proposed method outperforms the commonly used omnidirectional beampattern across various automotive scenarios, highlighting its superior adaptability and utility in multiple applications. In addition, our methods demonstrate good performance and computational efficiency, making them suitable for real-time 4-D imaging automotive BSUM radar applications.
本文提出了一种在四维成像汽车多输入多输出（MIMO）雷达中设计发射波形的方法，采用设计波形与期望波形之间的距离作为设计指标。利用$ell _{p}$-norm准则，我们考虑了更广泛的p值范围，特别是$p geq 2$和$0 lt p leq 1$，以增强优化框架。对于离散和连续相位约束，我们使用分块连续上界最小化（BSUM）技术有效地解决了在这些标准下提出的优化问题。我们的分析验证了目标函数的收敛性，并确认了解决方案的收敛性，从而为这一优化过程建立了新的停止准则。此外，我们还证明，在各种汽车应用场景中，我们提出的方法优于常用的全向蜂鸣器，突出了其在多种应用中的卓越适应性和实用性。此外，我们的方法表现出良好的性能和计算效率，使其适用于实时四维成像汽车 BSUM 雷达应用。
{"title":"Beampattern Shaping in 4-D Imaging Automotive MIMO Radars","authors":"Masoud Dorvash;Mahmoud Modarres-Hashemi;Mohammad Alaee-Kerahroodi","doi":"10.1109/TRS.2024.3443301","DOIUrl":"https://doi.org/10.1109/TRS.2024.3443301","url":null,"abstract":"This article presents a method for designing transmit beampattern in 4-D imaging automotive multiple-input-multiple-output (MIMO) radars, employing the distance between the designed and desired beampatterns as the design metric. Utilizing the \u0000<inline-formula> <tex-math>$ell _{p}$ </tex-math></inline-formula>\u0000-norm criteria, we consider a broader range of p values, specifically for \u0000<inline-formula> <tex-math>$p geq 2$ </tex-math></inline-formula>\u0000 and \u0000<inline-formula> <tex-math>$0 lt p leq 1$ </tex-math></inline-formula>\u0000, to enhance the optimization framework. The optimization problem formulated under these criteria is efficiently solved using the block successive upper bound minimization (BSUM) technique for discrete and continuous phase constraints. Our analysis verifies the convergence of the objective function and confirms the solution’s convergence, thereby establishing a new stopping criterion for this optimization process. Furthermore, we demonstrate that our proposed method outperforms the commonly used omnidirectional beampattern across various automotive scenarios, highlighting its superior adaptability and utility in multiple applications. In addition, our methods demonstrate good performance and computational efficiency, making them suitable for real-time 4-D imaging automotive BSUM radar applications.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"2 ","pages":"752-766"},"PeriodicalIF":0.0,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142159102","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-12DOI: 10.1109/TRS.2024.3442388
Edoardo Focante;Nitin Jonathan Myers;Geethu Joseph;Ashish Pandharipande
Radar is an important sensing modality that supports advanced levels of assisted and autonomous driving. In this work, we exploit side information, such as lane topology maps of the environment, position, and orientation information of the ego vehicle, to design beamformers in automotive radars. Specifically, we present a convex optimization-based method for transmit beamformer design using location-based static environment maps derived from georeferenced maps. The designed beams allocate less power along the directions where a static obstacle in the environment is closer and vice versa. We study the robustness of our situation-aware transmit beamforming technique to uncertainties in the position and orientation information of the ego vehicle. We also address these uncertainties by extending our situation-aware beamforming approach using tools from stochastic optimization (SO). Through simulations on the public dataset nuScenes, we show that our method achieves better detection than situation-agnostic radar sensing. Furthermore, our design is robust against errors in estimating the position and the orientation of the ego vehicle.
{"title":"Adaptive Beamforming for Situation-Aware Automotive Radars Under Uncertain Side Information","authors":"Edoardo Focante;Nitin Jonathan Myers;Geethu Joseph;Ashish Pandharipande","doi":"10.1109/TRS.2024.3442388","DOIUrl":"https://doi.org/10.1109/TRS.2024.3442388","url":null,"abstract":"Radar is an important sensing modality that supports advanced levels of assisted and autonomous driving. In this work, we exploit side information, such as lane topology maps of the environment, position, and orientation information of the ego vehicle, to design beamformers in automotive radars. Specifically, we present a convex optimization-based method for transmit beamformer design using location-based static environment maps derived from georeferenced maps. The designed beams allocate less power along the directions where a static obstacle in the environment is closer and vice versa. We study the robustness of our situation-aware transmit beamforming technique to uncertainties in the position and orientation information of the ego vehicle. We also address these uncertainties by extending our situation-aware beamforming approach using tools from stochastic optimization (SO). Through simulations on the public dataset nuScenes, we show that our method achieves better detection than situation-agnostic radar sensing. Furthermore, our design is robust against errors in estimating the position and the orientation of the ego vehicle.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"2 ","pages":"699-711"},"PeriodicalIF":0.0,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10634198","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142084495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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