Interrupted sampling repeater jamming (ISRJ) is an effective way to suppress normal radar probing in modern electronic warfare, and ISRJ suppression is essential in the radar signal processing stage. Accurately locating the position of the jamming signal is the first step to suppress the jamming signal. However, the traditional constant false alarm rate (CFAR) has performance loss with the characteristics of intensive distribution, extensive dynamic power range, and nonperiodical transmitting. This article proposes an ISRJ detection method based on a fusion Transformer and CFAR. First, the feature extraction model based on the Transformer is built to extract the continuous distribution features of ISRJ, forming rough detection results. Then, a CFAR detector is derived based on the rough detection result to estimate detector parameters. Third, the jamming features obtained by the Transformer, CFAR detector, and other features (such as instantaneous power and average power) are fused by a decision tree to realize robust detection in a low jamming-to-signal noise ratio (JSNR). Finally, we conducted simulated experiments to verify the effectiveness of the proposed method.
{"title":"Intensive Interrupted Sampling Repeater Jamming Detection Based on Transformer-CFAR Fusion Detection Model","authors":"Haonan Zhang;Shaopeng Wei;Song Wei;Lei Zhang;Peng Ren;Yejian Zhou","doi":"10.1109/TRS.2024.3465017","DOIUrl":"https://doi.org/10.1109/TRS.2024.3465017","url":null,"abstract":"Interrupted sampling repeater jamming (ISRJ) is an effective way to suppress normal radar probing in modern electronic warfare, and ISRJ suppression is essential in the radar signal processing stage. Accurately locating the position of the jamming signal is the first step to suppress the jamming signal. However, the traditional constant false alarm rate (CFAR) has performance loss with the characteristics of intensive distribution, extensive dynamic power range, and nonperiodical transmitting. This article proposes an ISRJ detection method based on a fusion Transformer and CFAR. First, the feature extraction model based on the Transformer is built to extract the continuous distribution features of ISRJ, forming rough detection results. Then, a CFAR detector is derived based on the rough detection result to estimate detector parameters. Third, the jamming features obtained by the Transformer, CFAR detector, and other features (such as instantaneous power and average power) are fused by a decision tree to realize robust detection in a low jamming-to-signal noise ratio (JSNR). Finally, we conducted simulated experiments to verify the effectiveness of the proposed method.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"2 ","pages":"936-949"},"PeriodicalIF":0.0,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368627","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 : 2024-09-17DOI: 10.1109/TRS.2024.3462471
Stephen Searle;Gottfried Lechner;Kutluyil Doğançay
Passive bistatic radar (PBR) employs an ambient source of radio frequency (RF) energy, such as a television transmitter, as an illuminator. The continuous nature of such transmissions results in significant interference in the surveillance signal, as direct-path (DP) transmission and returns from clutter. These must be suppressed in order to make target returns detectable. Delay-Doppler processing can be enhanced by demodulating and reconstructing the captured reference signal. However, target detectability is known to be affected when a reconstructed signal is used in the zero-Doppler cancellation (ZDC) process. This study proposes transmitter nonlinearity as a reason for poor cancellation. Analysis of ambiguity peak-to-floor measures suggests that under certain conditions unmodeled nonlinearity will cause degradation in ZDC. Several methods of nonlinearity estimation and modeling are proposed. Simulation evaluates these methods with various levels of nonlinearity and sensor noise. The methods are applied to ambiguity processing of terrestrial digital video broadcast (DVB-T) real data in both single-channel and two-channel receiver configurations. The results are explained with reference to the earlier analysis.
{"title":"Clutter Cancellation in Passive Bistatic Radar With Transmitter Nonlinearity","authors":"Stephen Searle;Gottfried Lechner;Kutluyil Doğançay","doi":"10.1109/TRS.2024.3462471","DOIUrl":"https://doi.org/10.1109/TRS.2024.3462471","url":null,"abstract":"Passive bistatic radar (PBR) employs an ambient source of radio frequency (RF) energy, such as a television transmitter, as an illuminator. The continuous nature of such transmissions results in significant interference in the surveillance signal, as direct-path (DP) transmission and returns from clutter. These must be suppressed in order to make target returns detectable. Delay-Doppler processing can be enhanced by demodulating and reconstructing the captured reference signal. However, target detectability is known to be affected when a reconstructed signal is used in the zero-Doppler cancellation (ZDC) process. This study proposes transmitter nonlinearity as a reason for poor cancellation. Analysis of ambiguity peak-to-floor measures suggests that under certain conditions unmodeled nonlinearity will cause degradation in ZDC. Several methods of nonlinearity estimation and modeling are proposed. Simulation evaluates these methods with various levels of nonlinearity and sensor noise. The methods are applied to ambiguity processing of terrestrial digital video broadcast (DVB-T) real data in both single-channel and two-channel receiver configurations. The results are explained with reference to the earlier analysis.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"2 ","pages":"979-990"},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142397288","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}
In this article, we investigate the joint estimation of range and velocity of targets using a wideband frequency-modulated continuous wave (FMCW) radar in the presence of range-Doppler coupling. To mitigate the effects of range-Doppler coupling, we propose a phase compensation framework based on a decoupled matrix atomic norm minimization (DANM). Subsequently, we propose a concave log-det heuristic to bridge the gap between atomic $ell _{0}$ -norm and atomic $ell _{1}$ -norm. To enhance the resolution, we use the proposed heuristic to formulate a reweighted decoupled 2-D matrix atomic norm (RMAN) minimization scheme and propose a semidefinite programming (SDP) solution for RMAN to decouple range and Doppler frequencies. Furthermore, we propose a novel RMAN-based approach for joint estimation of range and velocity of targets. The proposed algorithm is a gridless method that achieves resolution beyond the Rayleigh resolution limit and outperforms the conventional Fourier transform-based method in terms of estimation accuracy and root-mean-square error (RMSE), which converges to the derived Cramér-Rao lower bound (CRLB) as the signal-to-noise ratio (SNR) increases. The super-resolution ability of the proposed method is validated through extensive simulations under different scenarios.
{"title":"Super-Resolution Using FMCW Radar via Reweighted Decoupled Matrix Atomic Norm Minimization","authors":"Abhilash Gaur;Seshan Srirangarajan;Po-Hsuan Tseng;Kai-Ten Feng","doi":"10.1109/TRS.2024.3461208","DOIUrl":"https://doi.org/10.1109/TRS.2024.3461208","url":null,"abstract":"In this article, we investigate the joint estimation of range and velocity of targets using a wideband frequency-modulated continuous wave (FMCW) radar in the presence of range-Doppler coupling. To mitigate the effects of range-Doppler coupling, we propose a phase compensation framework based on a decoupled matrix atomic norm minimization (DANM). Subsequently, we propose a concave log-det heuristic to bridge the gap between atomic \u0000<inline-formula> <tex-math>$ell _{0}$ </tex-math></inline-formula>\u0000-norm and atomic \u0000<inline-formula> <tex-math>$ell _{1}$ </tex-math></inline-formula>\u0000-norm. To enhance the resolution, we use the proposed heuristic to formulate a reweighted decoupled 2-D matrix atomic norm (RMAN) minimization scheme and propose a semidefinite programming (SDP) solution for RMAN to decouple range and Doppler frequencies. Furthermore, we propose a novel RMAN-based approach for joint estimation of range and velocity of targets. The proposed algorithm is a gridless method that achieves resolution beyond the Rayleigh resolution limit and outperforms the conventional Fourier transform-based method in terms of estimation accuracy and root-mean-square error (RMSE), which converges to the derived Cramér-Rao lower bound (CRLB) as the signal-to-noise ratio (SNR) increases. The super-resolution ability of the proposed method is validated through extensive simulations under different scenarios.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"2 ","pages":"924-935"},"PeriodicalIF":0.0,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142328401","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 : 2024-09-12DOI: 10.1109/TRS.2024.3458150
Oleg A. Krasnov;Xingzhuo Li;Alexander Yarovoy
The problem of multicopter (multirotor drone) classification is considered. A two-stage approach for multicopter joint detection, tracking, and parameter estimation is proposed. A previously published particle filter (PF)-based track-before-detect (TBD) algorithm with a single-rotor drone is used in the first stage to detect, localize, and track the target. The algorithm is, however, modified by the utilization of a new drone model, which is based on a simplified representation of a rotated propeller as a bunch of thin wires. Using this model, closed-form analytical equations for the radar signal temporal dependence and micro-Doppler spectrum are derived for each rotor. Significant improvement in micro-Doppler spectrum prediction due to the implementation of this model has been observed. The actual number of multicopter rotors and their independent parameters, such as rotation velocity and initial orientation angle, are estimated in the second processing stage. The estimation problem is formulated as a maximum likelihood (ML) search in a multidimensional space of parameters. This computationally expensive optimization problem is converted to the problem of multiple likelihood function peaks detection in 2-D space “rotational velocity-initial orientation angle” for each propeller. The latter is solved by a computationally efficient 2-D grid search algorithm, which is followed by a few extra processing steps to remove the residual false alarms by analyzing detections over multiple consecutive coherence processing intervals. The proposed approach for multicopter detection and classification has been verified using simulated and experimental data.
{"title":"Model-Based Signal Processing for Joint Drones Detection, Tracking, and Parameters Estimation","authors":"Oleg A. Krasnov;Xingzhuo Li;Alexander Yarovoy","doi":"10.1109/TRS.2024.3458150","DOIUrl":"https://doi.org/10.1109/TRS.2024.3458150","url":null,"abstract":"The problem of multicopter (multirotor drone) classification is considered. A two-stage approach for multicopter joint detection, tracking, and parameter estimation is proposed. A previously published particle filter (PF)-based track-before-detect (TBD) algorithm with a single-rotor drone is used in the first stage to detect, localize, and track the target. The algorithm is, however, modified by the utilization of a new drone model, which is based on a simplified representation of a rotated propeller as a bunch of thin wires. Using this model, closed-form analytical equations for the radar signal temporal dependence and micro-Doppler spectrum are derived for each rotor. Significant improvement in micro-Doppler spectrum prediction due to the implementation of this model has been observed. The actual number of multicopter rotors and their independent parameters, such as rotation velocity and initial orientation angle, are estimated in the second processing stage. The estimation problem is formulated as a maximum likelihood (ML) search in a multidimensional space of parameters. This computationally expensive optimization problem is converted to the problem of multiple likelihood function peaks detection in 2-D space “rotational velocity-initial orientation angle” for each propeller. The latter is solved by a computationally efficient 2-D grid search algorithm, which is followed by a few extra processing steps to remove the residual false alarms by analyzing detections over multiple consecutive coherence processing intervals. The proposed approach for multicopter detection and classification has been verified using simulated and experimental data.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"2 ","pages":"880-898"},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142324329","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}
In this article, we consider the problem of developing a computational model for emulating an RF channel. The motivation for this is that an accurate and scalable emulator has the potential to minimize the need for field testing, which is expensive, slow, and difficult to replicate. Traditionally, emulators are built using a tapped delay line (TDL) model where long filters modeling the physical interactions of objects are implemented directly. For an emulation scenario consisting of M objects all interacting with one another, the TDL model’s computational requirements scale as $O(M^{3})$ per sample: there are $O(M^{2})$ channels, each with $O(M)$ complexity. In this article, we develop a new “direct path” model that, while remaining physically faithful, allows us to carefully factor the emulator operations, resulting in an $O(M^{2})$ per-sample scaling of the computational requirements. The impact of this is drastic, a 200-object scenario sees about a $100times $ reduction in the number of per-sample computations. Furthermore, the direct path model gives us a natural way to distribute the computations for an emulation: each object is mapped to a computational node, and these nodes are networked in a fully connected communication graph. Alongside a discussion of the model and the physical phenomena it emulates, we show how to efficiently parameterize antenna responses and scattering profiles within this direct path framework. To verify the model and demonstrate its viability in hardware, we provide several numerical experiments produced using a cycle-level C++ simulator of a hardware implementation of the model.
在本文中，我们探讨了开发射频信道仿真计算模型的问题。这样做的动机是，精确且可扩展的仿真器有可能最大限度地减少现场测试的需求，而现场测试成本高、速度慢且难以复制。传统上，仿真器是使用分接延迟线（TDL）模型构建的，在该模型中直接实现了模拟对象物理交互的长滤波器。对于由 M 个相互影响的对象组成的仿真场景，TDL 模型的计算要求按每个样本 $O(M^{3})$ 的比例缩放：有 $O(M^{2})$ 个通道，每个通道的复杂度为 $O(M)$。在本文中，我们开发了一种新的 "直接路径 "模型，在保持物理忠实性的同时，允许我们仔细考虑仿真器操作的因素，从而使每个样本的计算要求缩放为 $O(M^{2})$。这带来的影响是巨大的，在一个有 200 个对象的场景中，每个样本的计算量减少了约 $100times$。此外，直接路径模型为我们提供了一种分配仿真计算的自然方法：每个对象映射到一个计算节点，这些节点在一个完全连接的通信图中联网。在讨论该模型及其模拟的物理现象的同时，我们还展示了如何在此直接路径框架内有效地对天线响应和散射剖面进行参数化。为了验证该模型并证明其在硬件中的可行性，我们提供了几个使用该模型硬件实现的循环级 C++ 模拟器进行的数值实验。
{"title":"Real-Time Digital RF Emulation—Part I: The Direct Path Computational Model","authors":"C. DeLude;J. Driscoll;M. Mukherjee;N. Rahman;X. Mao;U. Kamal;S. Khan;H. Sivaraman;E. Huang;J. McHarg;M. Swaminathan;S. Pande;S. Mukhopadhyay;J. Romberg","doi":"10.1109/TRS.2024.3457491","DOIUrl":"https://doi.org/10.1109/TRS.2024.3457491","url":null,"abstract":"In this article, we consider the problem of developing a computational model for emulating an RF channel. The motivation for this is that an accurate and scalable emulator has the potential to minimize the need for field testing, which is expensive, slow, and difficult to replicate. Traditionally, emulators are built using a tapped delay line (TDL) model where long filters modeling the physical interactions of objects are implemented directly. For an emulation scenario consisting of M objects all interacting with one another, the TDL model’s computational requirements scale as \u0000<inline-formula> <tex-math>$O(M^{3})$ </tex-math></inline-formula>\u0000 per sample: there are \u0000<inline-formula> <tex-math>$O(M^{2})$ </tex-math></inline-formula>\u0000 channels, each with \u0000<inline-formula> <tex-math>$O(M)$ </tex-math></inline-formula>\u0000 complexity. In this article, we develop a new “direct path” model that, while remaining physically faithful, allows us to carefully factor the emulator operations, resulting in an \u0000<inline-formula> <tex-math>$O(M^{2})$ </tex-math></inline-formula>\u0000 per-sample scaling of the computational requirements. The impact of this is drastic, a 200-object scenario sees about a \u0000<inline-formula> <tex-math>$100times $ </tex-math></inline-formula>\u0000 reduction in the number of per-sample computations. Furthermore, the direct path model gives us a natural way to distribute the computations for an emulation: each object is mapped to a computational node, and these nodes are networked in a fully connected communication graph. Alongside a discussion of the model and the physical phenomena it emulates, we show how to efficiently parameterize antenna responses and scattering profiles within this direct path framework. To verify the model and demonstrate its viability in hardware, we provide several numerical experiments produced using a cycle-level C++ simulator of a hardware implementation of the model.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"2 ","pages":"866-879"},"PeriodicalIF":0.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142316325","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}
A near memory hardware accelerator, based on a novel direct path computational model (DPCM), for real-time emulation of radio frequency (RF) systems is demonstrated. Our evaluation of hardware performance uses both application-specific integrated circuits (ASICs) and field programmable gate array (FPGA) methodologies: 1) the ASIC test-chip implementation, using Taiwan Semiconductor Manufacturing Company (TSMC) 28-nm CMOS, leverages distributed autonomous control to extract concurrence in compute as well as low latency. It achieves a 518 MHz per channel bandwidth in a prototype four-node system. The maximum emulation range supported in this paradigm is 9.5 km with $0.24~mu $ s of per-sample emulation latency and 2) the FPGA-based implementation, evaluated on a Xilinx ZCU104 board, demonstrates a nine-node test case (two transmitters, one receiver, and six passive reflectors) with an emulation range of 1.13–27.3 km at 215-MHz bandwidth.
{"title":"Real-Time Digital RF Emulation—Part II: A Near Memory Custom Accelerator","authors":"X. Mao;M. Mukherjee;N. Mizanur Rahman;C. DeLude;J. Driscoll;S. Sharma;P. Behnam;U. Kamal;J. Woo;D. Kim;S. Khan;J. Tong;J. Seo;P. Sinha;M. Swaminathan;T. Krishna;S. Pande;J. Romberg;S. Mukhopadhyay","doi":"10.1109/TRS.2024.3457523","DOIUrl":"https://doi.org/10.1109/TRS.2024.3457523","url":null,"abstract":"A near memory hardware accelerator, based on a novel direct path computational model (DPCM), for real-time emulation of radio frequency (RF) systems is demonstrated. Our evaluation of hardware performance uses both application-specific integrated circuits (ASICs) and field programmable gate array (FPGA) methodologies: 1) the ASIC test-chip implementation, using Taiwan Semiconductor Manufacturing Company (TSMC) 28-nm CMOS, leverages distributed autonomous control to extract concurrence in compute as well as low latency. It achieves a 518 MHz per channel bandwidth in a prototype four-node system. The maximum emulation range supported in this paradigm is 9.5 km with \u0000<inline-formula> <tex-math>$0.24~mu $ </tex-math></inline-formula>\u0000s of per-sample emulation latency and 2) the FPGA-based implementation, evaluated on a Xilinx ZCU104 board, demonstrates a nine-node test case (two transmitters, one receiver, and six passive reflectors) with an emulation range of 1.13–27.3 km at 215-MHz bandwidth.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"2 ","pages":"849-865"},"PeriodicalIF":0.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142324255","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}
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}
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