Pub Date : 2024-06-28DOI: 10.1109/TRS.2024.3420551
Nanjun Li;Fulai Wang;Chen Pang;Yongzhen Li
Aimed to enhance the robustness of simultaneously polarimetric radar (SPR) systems against interrupted-sampling repeater jamming (ISRJ) attacks by polarization and waveform diversity techniques, this article focuses on the joint design of unimodular sequences and receiving filters for jamming suppression. Specifically, we first develop a dynamic signal processing framework for target polarization scattering matrix (PSM) estimation based on their statistical properties and orthogonal conditions between receiving filters and ISRJ signals. Then, the weighted sum of integrated sidelobe levels (ISLs) of auto- and cross-ambiguity functions and signal-to-noise ratio losses (SNRLs) is minimized with unimodular and energy constraints to mitigate ISRJ in SPR systems considering both single pulse and multipulse. To handle the resulting NP-hard design problems, an iterative optimization method capitalizing on the alternating direction and majorization-minimization (MM) framework is proposed. The numerical results are provided to show the superiority of the proposed algorithm in comparison with a pair of linear frequency modulation (LFM) waveforms with opposite slopes with respect to the anti-ISRJ performance.
{"title":"Design of Doppler Resilient Sequences and Receiving Filters Against Interrupted-Sampling Repeater Jamming for Simultaneously Polarimetric Radars","authors":"Nanjun Li;Fulai Wang;Chen Pang;Yongzhen Li","doi":"10.1109/TRS.2024.3420551","DOIUrl":"https://doi.org/10.1109/TRS.2024.3420551","url":null,"abstract":"Aimed to enhance the robustness of simultaneously polarimetric radar (SPR) systems against interrupted-sampling repeater jamming (ISRJ) attacks by polarization and waveform diversity techniques, this article focuses on the joint design of unimodular sequences and receiving filters for jamming suppression. Specifically, we first develop a dynamic signal processing framework for target polarization scattering matrix (PSM) estimation based on their statistical properties and orthogonal conditions between receiving filters and ISRJ signals. Then, the weighted sum of integrated sidelobe levels (ISLs) of auto- and cross-ambiguity functions and signal-to-noise ratio losses (SNRLs) is minimized with unimodular and energy constraints to mitigate ISRJ in SPR systems considering both single pulse and multipulse. To handle the resulting NP-hard design problems, an iterative optimization method capitalizing on the alternating direction and majorization-minimization (MM) framework is proposed. The numerical results are provided to show the superiority of the proposed algorithm in comparison with a pair of linear frequency modulation (LFM) waveforms with opposite slopes with respect to the anti-ISRJ performance.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"2 ","pages":"661-676"},"PeriodicalIF":0.0,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141602422","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 : 2024-06-24DOI: 10.1109/TRS.2024.3418461
Guanqun Sun;Fangzheng Zhang;Xiaoyue Yu;Yuewen Zhou;Yuhui He;Xing Wang;Shilong Pan
A photonics-based broadband single-input-multiple-output (SIMO)-orbital angular momentum (OAM) radar is proposed to implement high-resolution radar imaging. In the transmitter, a broadband linear frequency-modulated (LFM) signal is generated by an optically injected semiconductor laser and emitted by a single antenna to illuminate the target. In the receiver, a uniform circular array (UCA) collects the echoes and introduces OAM modulations before photonic frequency mixing is implemented for broadband dechirp processing. The use of microwave photonic techniques enlarges the operation bandwidth and thus improves the radar range resolution, while the SIMO structure mitigates the OAM beam divergence and energy hollow problems. Based on this SIMO-OAM radar, a super-resolution imaging method with random OAM modulation and coincidence processing is proposed to break through the azimuth resolution limitation. A proof-of-concept photonics-based $1times 16$ OAM radar is established with an 8-GHz (18–26 GHz) bandwidth, of which the range resolution reaches 2.1 cm. By using the proposed imaging method, super-resolution imaging with six times higher azimuth resolution than traditional OAM radar is achieved. In the experiment, high-resolution imaging of small-size complex targets is successfully demonstrated, verifying that the proposed system and imaging method can meet the requirement for high-resolution radar detection and high-precision target recognition.
{"title":"Photonics-Based Broadband Single-Input-Multiple- Output-OAM Coincidence Imaging","authors":"Guanqun Sun;Fangzheng Zhang;Xiaoyue Yu;Yuewen Zhou;Yuhui He;Xing Wang;Shilong Pan","doi":"10.1109/TRS.2024.3418461","DOIUrl":"https://doi.org/10.1109/TRS.2024.3418461","url":null,"abstract":"A photonics-based broadband single-input-multiple-output (SIMO)-orbital angular momentum (OAM) radar is proposed to implement high-resolution radar imaging. In the transmitter, a broadband linear frequency-modulated (LFM) signal is generated by an optically injected semiconductor laser and emitted by a single antenna to illuminate the target. In the receiver, a uniform circular array (UCA) collects the echoes and introduces OAM modulations before photonic frequency mixing is implemented for broadband dechirp processing. The use of microwave photonic techniques enlarges the operation bandwidth and thus improves the radar range resolution, while the SIMO structure mitigates the OAM beam divergence and energy hollow problems. Based on this SIMO-OAM radar, a super-resolution imaging method with random OAM modulation and coincidence processing is proposed to break through the azimuth resolution limitation. A proof-of-concept photonics-based \u0000<inline-formula> <tex-math>$1times 16$ </tex-math></inline-formula>\u0000 OAM radar is established with an 8-GHz (18–26 GHz) bandwidth, of which the range resolution reaches 2.1 cm. By using the proposed imaging method, super-resolution imaging with six times higher azimuth resolution than traditional OAM radar is achieved. In the experiment, high-resolution imaging of small-size complex targets is successfully demonstrated, verifying that the proposed system and imaging method can meet the requirement for high-resolution radar detection and high-precision target recognition.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"2 ","pages":"690-698"},"PeriodicalIF":0.0,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141965319","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 : 2024-06-24DOI: 10.1109/TRS.2024.3418689
Adib Y. Nashashibi;Tanner J. Douglas;Mani Kashanianfard;Stephen W. Decker;Kamal Sarabandi
This article presents a fully polarimetric instrumentation radar designed for conducting phenomenological studies in support of autonomous vehicle research at W-band frequencies. The compact radar utilizes a unique single-antenna system with a narrow beamwidth of 1.1° (two-way) and better than 45-dB isolation between the transmit and receive signal paths. It employs the frequency-modulated continuous-wave (FMCW) modulation scheme and operates over a maximum bandwidth of 5.5 GHz centered around 79.25 GHz. The radar benefits from a low phase noise level of −86 dBc/Hz at 10-kHz offset and its receiver channels have an effective noise figure value of 2.7 dB. The calibrated radar can be used to accurately measure the polarimetric response of both point and distributed targets. It can also generate high-resolution range-velocity maps of moving targets in a given traffic scene. The radar was used to characterize the polarimetric response of slow-moving targets, such as pedestrians and bicycles. The statistics of the radar cross sections (RCSs) of these targets were computed, and unique features observed in the range-velocity maps of these targets were revealed.
{"title":"High-Resolution Polarimetric Radar for Autonomous Vehicle Research at W-Band Frequencies","authors":"Adib Y. Nashashibi;Tanner J. Douglas;Mani Kashanianfard;Stephen W. Decker;Kamal Sarabandi","doi":"10.1109/TRS.2024.3418689","DOIUrl":"https://doi.org/10.1109/TRS.2024.3418689","url":null,"abstract":"This article presents a fully polarimetric instrumentation radar designed for conducting phenomenological studies in support of autonomous vehicle research at W-band frequencies. The compact radar utilizes a unique single-antenna system with a narrow beamwidth of 1.1° (two-way) and better than 45-dB isolation between the transmit and receive signal paths. It employs the frequency-modulated continuous-wave (FMCW) modulation scheme and operates over a maximum bandwidth of 5.5 GHz centered around 79.25 GHz. The radar benefits from a low phase noise level of −86 dBc/Hz at 10-kHz offset and its receiver channels have an effective noise figure value of 2.7 dB. The calibrated radar can be used to accurately measure the polarimetric response of both point and distributed targets. It can also generate high-resolution range-velocity maps of moving targets in a given traffic scene. The radar was used to characterize the polarimetric response of slow-moving targets, such as pedestrians and bicycles. The statistics of the radar cross sections (RCSs) of these targets were computed, and unique features observed in the range-velocity maps of these targets were revealed.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"2 ","pages":"632-644"},"PeriodicalIF":0.0,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141583622","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 : 2024-06-21DOI: 10.1109/TRS.2024.3417519
Rahul Sharma;Okan Yurduseven
Accurately characterizing material properties, particularly the spatial distribution of permittivity, is crucial across diverse domains such as medical imaging, nondestructive testing, and materials science. This work introduces an innovative strategy to tackle the inverse problem of deducing the permittivity distribution within a medium by leveraging time-dependent data of the electric field distribution. The approach utilizes a neural network trained with guidance from the wave equation, embedding the fundamental physics of wave propagation within the network architecture. This integration empowers the network to assimilate domain-specific knowledge during training, combining deep learning capabilities with physics-based constraints. This hybrid framework establishes a robust relationship between the time changing electric field distribution and the underlying permittivity distribution, effectively solving the complex inverse problem. By training on a comprehensive dataset, the neural network discerns intricate variations in spatial permittivity from the intricate temporal evolution of the electric field. Results validate the effectiveness of this approach, showcasing impressive accuracy in the reconstruction of the permittivity distribution.
{"title":"Quantitative Microwave Imaging Using Deep Learning Network Guided by Plane Wave Equation","authors":"Rahul Sharma;Okan Yurduseven","doi":"10.1109/TRS.2024.3417519","DOIUrl":"https://doi.org/10.1109/TRS.2024.3417519","url":null,"abstract":"Accurately characterizing material properties, particularly the spatial distribution of permittivity, is crucial across diverse domains such as medical imaging, nondestructive testing, and materials science. This work introduces an innovative strategy to tackle the inverse problem of deducing the permittivity distribution within a medium by leveraging time-dependent data of the electric field distribution. The approach utilizes a neural network trained with guidance from the wave equation, embedding the fundamental physics of wave propagation within the network architecture. This integration empowers the network to assimilate domain-specific knowledge during training, combining deep learning capabilities with physics-based constraints. This hybrid framework establishes a robust relationship between the time changing electric field distribution and the underlying permittivity distribution, effectively solving the complex inverse problem. By training on a comprehensive dataset, the neural network discerns intricate variations in spatial permittivity from the intricate temporal evolution of the electric field. Results validate the effectiveness of this approach, showcasing impressive accuracy in the reconstruction of the permittivity distribution.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"2 ","pages":"607-617"},"PeriodicalIF":0.0,"publicationDate":"2024-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141495026","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}
Effective monitoring of vital signs is a fundamental aspect of healthcare. To measure vital signs, patients often hesitate to wear probes and body-worn sensors for extended periods because these devices can limit their movement and cause discomfort. In this study, we present three radar-based techniques to estimate vital signs and underlying muscle expansion. The first method employs a short-time Fourier transform (STFT), but it has limitations due to its fixed resolution and its performance dependency on the carrier frequency. The second method modifies the Hilbert-Huang transform (HHT) to address the mode-mixing problem. The HHT breaks down the signal into its fundamental components. By subsequently applying Fourier transform and signal filtering, we demonstrate its feasibility of estimating heartbeat and breathing rates. In our latest method, which constitutes the primary contribution of this study, we exploit the repetitive patterns inherent in both heartbeat and breathing signals. This involves representing the spectrum of the received signal as a discrete frequency spectrum and subsequently applying harmonic accumulation. Our simulation results consistently demonstrate that the harmonics accumulation (HA) algorithm outperforms other algorithms in terms of accuracy and effectiveness. To assess the performance of our suggested algorithms, we derive the Cramér-Rao lower bound (CRLB) as a benchmark. Our results show the effectiveness of the proposed methods.
{"title":"Discovering the Unseen: Radar-Based Estimation of Heartbeat, Breathing Rate, and Underlying Muscle Expansion Without Probes","authors":"Sajid Ahmed;Pratiti Paul;Tharmalingam Ratnarajah;Mohamed-Slim Alouini","doi":"10.1109/TRS.2024.3412915","DOIUrl":"https://doi.org/10.1109/TRS.2024.3412915","url":null,"abstract":"Effective monitoring of vital signs is a fundamental aspect of healthcare. To measure vital signs, patients often hesitate to wear probes and body-worn sensors for extended periods because these devices can limit their movement and cause discomfort. In this study, we present three radar-based techniques to estimate vital signs and underlying muscle expansion. The first method employs a short-time Fourier transform (STFT), but it has limitations due to its fixed resolution and its performance dependency on the carrier frequency. The second method modifies the Hilbert-Huang transform (HHT) to address the mode-mixing problem. The HHT breaks down the signal into its fundamental components. By subsequently applying Fourier transform and signal filtering, we demonstrate its feasibility of estimating heartbeat and breathing rates. In our latest method, which constitutes the primary contribution of this study, we exploit the repetitive patterns inherent in both heartbeat and breathing signals. This involves representing the spectrum of the received signal as a discrete frequency spectrum and subsequently applying harmonic accumulation. Our simulation results consistently demonstrate that the harmonics accumulation (HA) algorithm outperforms other algorithms in terms of accuracy and effectiveness. To assess the performance of our suggested algorithms, we derive the Cramér-Rao lower bound (CRLB) as a benchmark. Our results show the effectiveness of the proposed methods.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"2 ","pages":"594-606"},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141474936","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 describes a method of deriving and verifying hardware specification of a direct digital frequency synthesizer (DDFS) and radio frequency digital-to-analog converter (RFDAC)-based frequency-modulated continuous-wave (FMCW) modulator. The analysis of the concept is conducted by studying the digital nonlinearities, such as amplitude quantization noise, phase quantization noise, and frequency error in the ramp, and analog nonlinearities, such as IQ quadrature error and counter inter modulation-3 (CIM3) of the RFDAC. The impact of the nonlinearities on the detectability of target in the intermediate frequency (IF) spectrum is evaluated with the MATLAB model of the frequency modulator. The outcome of the concept evaluation predicts the low-level hardware specifications needed for the design such as amplitude quantization, phase quantization, expected noise level, spur positions in the target IF spectrum, and frequency error in the ramp. The RFDAC-based FMCW modulator is manufactured in 28-nm technology with the derived parameters and the time-domain data of a frequency ramp from 5 to 9-GHz in 100 $mu $ s is sampled during measurement. The data are postprocessed to confirm the predictions made by the simulation model and to characterize ramp linearity, dynamic phase noise (DPN), and settling time of the ramp. The frequency error for a 4-GHz ramp in 100- $mu $ s duration is ±100kHz, and the settling time in the postprocessed result is in the 20-ns range.
{"title":"Concept Evaluation of a DDFS and RFDAC-Based FMCW Modulator","authors":"Soumya Krishnapuram Sireesh;Niels Christoffers;Christoph Wagner;Andreas Stelzer","doi":"10.1109/TRS.2024.3410137","DOIUrl":"https://doi.org/10.1109/TRS.2024.3410137","url":null,"abstract":"This article describes a method of deriving and verifying hardware specification of a direct digital frequency synthesizer (DDFS) and radio frequency digital-to-analog converter (RFDAC)-based frequency-modulated continuous-wave (FMCW) modulator. The analysis of the concept is conducted by studying the digital nonlinearities, such as amplitude quantization noise, phase quantization noise, and frequency error in the ramp, and analog nonlinearities, such as IQ quadrature error and counter inter modulation-3 (CIM3) of the RFDAC. The impact of the nonlinearities on the detectability of target in the intermediate frequency (IF) spectrum is evaluated with the MATLAB model of the frequency modulator. The outcome of the concept evaluation predicts the low-level hardware specifications needed for the design such as amplitude quantization, phase quantization, expected noise level, spur positions in the target IF spectrum, and frequency error in the ramp. The RFDAC-based FMCW modulator is manufactured in 28-nm technology with the derived parameters and the time-domain data of a frequency ramp from 5 to 9-GHz in 100\u0000<inline-formula> <tex-math>$mu $ </tex-math></inline-formula>\u0000 s is sampled during measurement. The data are postprocessed to confirm the predictions made by the simulation model and to characterize ramp linearity, dynamic phase noise (DPN), and settling time of the ramp. The frequency error for a 4-GHz ramp in 100-\u0000<inline-formula> <tex-math>$mu $ </tex-math></inline-formula>\u0000 s duration is ±100kHz, and the settling time in the postprocessed result is in the 20-ns range.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"2 ","pages":"618-631"},"PeriodicalIF":0.0,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141539221","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 : 2024-06-03DOI: 10.1109/TRS.2024.3409029
Matthew G. Gaydos;David J. Love;Taejoon Kim
Multiple-input-multiple-output (MIMO) radar systems have become a heavily researched topic in recent years due to the improved diversity techniques when compared to that of 1-D radar waveforms. Although there exist a myriad of techniques to design the transmit power distribution for MIMO radar systems, stringent constant modulus power limitations imposed by modern high-power amplifiers (HPAs) complicate their practical implementation. Ideally, a technique that emulates the spatial filtering flexibility of precoded MIMO communications is desired. To achieve such flexibility, an MIMO radar framework must be introduced that guarantees constant modulus for all combinations of signal sets and precoders, allowing simple interchangeability between components of the waveform—whether that be a new transmit power distribution or an adjusted dynamic MIMO radar waveform. In this article, we show that designing a constant modulus precoded MIMO radar can be achieved in a practical manner, utilizing alphabet-based waveform construction. This technique leverages the use of two sets for which the product of any pair of vectors, one from each alphabet, guarantees a fixed constant. By utilizing these sets as alphabets to design the waveform, it is possible to implement MIMO radar waveforms of any rank and enable the decoupling of the precoder and MIMO radar waveform design. This article presents a framework that achieves the aforementioned requirements by utilizing the properties of Zadoff-Chu (ZC) sequences and the discrete Fourier transform (DFT) matrix. By restricting construction of the precoder and signal set to be in part formed from finite alphabets, it is shown that the constant modulus constraint is achieved for any precoder and any signal set combination.
与一维雷达波形相比，多输入多输出（MIMO）雷达系统改进了分集技术，因此成为近年来研究的热点。虽然有无数种技术可用于设计 MIMO 雷达系统的发射功率分布，但现代大功率放大器（HPA）施加的严格恒定模数功率限制使其实际应用变得复杂。理想情况下，我们需要一种技术来模拟预编码多输入多输出通信的空间滤波灵活性。要实现这种灵活性，必须引入一种 MIMO 雷达框架，它能保证信号集和预编码器的所有组合都具有恒定的模数，并允许波形各组成部分之间的简单互换--无论是新的发射功率分布还是调整后的动态 MIMO 雷达波形。在本文中，我们将展示如何利用基于字母的波形构造，以实用的方式设计恒定模预编码 MIMO 雷达。这种技术利用了两个集合，其中任何一对矢量的乘积（来自每个字母表的一个矢量）都能保证一个固定常数。利用这些集合作为字母表来设计波形，就有可能实现任何等级的 MIMO 雷达波形，并实现前置编码器和 MIMO 雷达波形设计的解耦。本文提出了一个框架，利用扎多夫-楚（ZC）序列和离散傅立叶变换（DFT）矩阵的特性来实现上述要求。通过限制前置编码器和信号集的构造部分由有限字母组成，证明了恒定模数约束可在任何前置编码器和任何信号集组合中实现。
{"title":"Constant Modulus Precoded MIMO Radar Based on Zadoff-Chu Sequences","authors":"Matthew G. Gaydos;David J. Love;Taejoon Kim","doi":"10.1109/TRS.2024.3409029","DOIUrl":"https://doi.org/10.1109/TRS.2024.3409029","url":null,"abstract":"Multiple-input-multiple-output (MIMO) radar systems have become a heavily researched topic in recent years due to the improved diversity techniques when compared to that of 1-D radar waveforms. Although there exist a myriad of techniques to design the transmit power distribution for MIMO radar systems, stringent constant modulus power limitations imposed by modern high-power amplifiers (HPAs) complicate their practical implementation. Ideally, a technique that emulates the spatial filtering flexibility of precoded MIMO communications is desired. To achieve such flexibility, an MIMO radar framework must be introduced that guarantees constant modulus for all combinations of signal sets and precoders, allowing simple interchangeability between components of the waveform—whether that be a new transmit power distribution or an adjusted dynamic MIMO radar waveform. In this article, we show that designing a constant modulus precoded MIMO radar can be achieved in a practical manner, utilizing alphabet-based waveform construction. This technique leverages the use of two sets for which the product of any pair of vectors, one from each alphabet, guarantees a fixed constant. By utilizing these sets as alphabets to design the waveform, it is possible to implement MIMO radar waveforms of any rank and enable the decoupling of the precoder and MIMO radar waveform design. This article presents a framework that achieves the aforementioned requirements by utilizing the properties of Zadoff-Chu (ZC) sequences and the discrete Fourier transform (DFT) matrix. By restricting construction of the precoder and signal set to be in part formed from finite alphabets, it is shown that the constant modulus constraint is achieved for any precoder and any signal set combination.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"2 ","pages":"677-689"},"PeriodicalIF":0.0,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141602504","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 comprehensive method for radar-camera calibration with a primary focus on real-time projection, addressing the critical need for precise spatial and temporal alignment between radar and camera sensor modalities. The research introduces a novel methodology for calibration utilizing geometrical transformation, incorporating radar corner reflectors to establish correspondences. This methodology applies to post-automotive manufacturing for integration into radar-camera applications such as advanced driver-assistance systems (ADASs), adaptive cruise control (ACC), collision warning, and mitigation systems. It also serves post-production for sensor installation and algorithm development. The proposed approach employs an advanced algorithm to optimize spatial and temporal synchronization and radar and camera data alignment, ensuring accuracy in multimodal sensor fusion. Rigorous validation through extensive testing demonstrates the efficiency and reliability of the proposed system. The results show that the calibration method is highly accurate compared to the existing state-of-the-art methods, with minimal errors, an average Euclidean distance (AED) of 1.447, and a root-mean-square reprojection error (RMSRE) of (0.1720, 0.5965), indicating a highly efficient spatial synchronization method. During real-time projection, the proposed algorithm for temporal synchronization achieves an average latency of 35 ms between frames.
{"title":"An Efficient Approach for Calibration of Automotive Radar–Camera With Real-Time Projection of Multimodal Data","authors":"Nitish Kumar;Ayush Dasgupta;Venkata Satyanand Mutnuri;Rajalakshmi Pachamuthu","doi":"10.1109/TRS.2024.3408231","DOIUrl":"https://doi.org/10.1109/TRS.2024.3408231","url":null,"abstract":"This article presents a comprehensive method for radar-camera calibration with a primary focus on real-time projection, addressing the critical need for precise spatial and temporal alignment between radar and camera sensor modalities. The research introduces a novel methodology for calibration utilizing geometrical transformation, incorporating radar corner reflectors to establish correspondences. This methodology applies to post-automotive manufacturing for integration into radar-camera applications such as advanced driver-assistance systems (ADASs), adaptive cruise control (ACC), collision warning, and mitigation systems. It also serves post-production for sensor installation and algorithm development. The proposed approach employs an advanced algorithm to optimize spatial and temporal synchronization and radar and camera data alignment, ensuring accuracy in multimodal sensor fusion. Rigorous validation through extensive testing demonstrates the efficiency and reliability of the proposed system. The results show that the calibration method is highly accurate compared to the existing state-of-the-art methods, with minimal errors, an average Euclidean distance (AED) of 1.447, and a root-mean-square reprojection error (RMSRE) of (0.1720, 0.5965), indicating a highly efficient spatial synchronization method. During real-time projection, the proposed algorithm for temporal synchronization achieves an average latency of 35 ms between frames.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"2 ","pages":"573-582"},"PeriodicalIF":0.0,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141315167","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 : 2024-04-29DOI: 10.1109/TRS.2024.3394896
Augusto Aubry;Antonio De Maio;Luca Pallotta
The modern battlefield scenario is strongly influenced by the innovative capabilities of multifunction phased array radars (MPARs), which can perform a plethora of sensing and communication (COM) activities sequentially or in parallel. In fact, the MPAR can functionally cluster its phased array into bespoke subapertures implementing different tasks. Accordingly, a portion of the other available resources, e.g., bandwidth, power-aperture product (PAP), and time, is also assigned to each subaperture, and the grand challenge is the definition of strategies for optimal scheduling of the tasks to be executed. In this respect, a rule-based algorithm for task scheduling is proposed in this article. In a nutshell, in each time window, the procedure first allocates the radar tasks (viz., volume search, cued search, update, and confirmation tracking) and then utilizes the COM looks to fill the empty intraslot time left by the radar tasks. When there are two concurrent looks, the allocation is performed according to their priorities. Moreover, if the bandwidth and PAP are sufficient, some of them can be also scheduled in parallel. Interesting results in terms of bandwidth and time occupancy efficiency are observed from simulations conducted in challenging scenarios comprising also multiple maneuvering targets.
多功能相控阵雷达（MPAR）的创新能力对现代战场场景产生了重大影响，它可以连续或并行地执行大量传感和通信（COM）活动。事实上，MPAR 可以在功能上将其相控阵集群为执行不同任务的定制子孔径。因此，其他可用资源（如带宽、功率-孔径乘积（PAP）和时间）的一部分也被分配给每个子孔径，而最大的挑战在于如何定义待执行任务的优化调度策略。为此，本文提出了一种基于规则的任务调度算法。简而言之，在每个时间窗口中，该程序首先分配雷达任务（即体积搜索、提示搜索、更新和确认跟踪），然后利用 COM 观测来填补雷达任务留下的空隙内时间。当有两个并发观测任务时，将根据它们的优先级进行分配。此外，如果带宽和 PAP 足够，其中一些任务也可以并行调度。通过在具有挑战性的场景（包括多个机动目标）中进行模拟，在带宽和时间占用效率方面观察到了有趣的结果。
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Pub Date : 2024-04-22DOI: 10.1109/TRS.2024.3392439
Paulo Ricardo Marques de Araujo;Aboelmagd Noureldin;Sidney Givigi
This paper presents a deep learning framework for the estimation of land vehicle ego-velocity using Frequency Modulated Continuous Wave (FMCW) automotive radars, addressing the challenges of data sparsity and noise without the need for extrinsic radar calibration. By structuring radar scans into image-based and voxel-based networks, our approach demonstrates robust ego-velocity estimation across multiple sensor configurations and orientations. Experimental results from three distinct datasets—RadarScenes, NavINST, and MSC-RAD4R—validate the framework’s effectiveness, showing superior performance over traditional methods. The models’ adaptability to various sensor specifications and their computational efficiency highlight their potential for real-time applications. We made our implementation open-source at: https://github.com/paaraujo/deep-ego-velocity .
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