Pub Date : 2024-10-01DOI: 10.1109/TRS.2024.3471696
Hiroki Mori;Ryota Sekiya
Some existing radar imaging apparatuses require a large number of transmitting and receiving antennas and, thus, impose stringent requirements on hardware design. In this article, we propose a millimeter-wave radar imaging method that combines multistatic radar with coprime measurements, to significantly reduce the number of antennas and the amount of data. The proposed radar array system replaces every monostatic radar with a pair comprising a separated transmitter and receiver along with phase corrections. Since multiple receivers can simultaneously receive the reflection when a transmitter emits a signal and then efficiently create virtual subarrays obtained by coprime measurements, the proposed radar array system can further reduce the number of measurements (antennas) and the amount of data compared with the existing schemes. Our proposal is demonstrated through simulations and experiments, and the results indicate that the proposed radar array system is advantageous in implementation in terms of hardware design and data acquisition time.
{"title":"Millimeter-Wave Radar Imaging Using Multistatic Coprime Array Configuration for Invisible Object Testing","authors":"Hiroki Mori;Ryota Sekiya","doi":"10.1109/TRS.2024.3471696","DOIUrl":"https://doi.org/10.1109/TRS.2024.3471696","url":null,"abstract":"Some existing radar imaging apparatuses require a large number of transmitting and receiving antennas and, thus, impose stringent requirements on hardware design. In this article, we propose a millimeter-wave radar imaging method that combines multistatic radar with coprime measurements, to significantly reduce the number of antennas and the amount of data. The proposed radar array system replaces every monostatic radar with a pair comprising a separated transmitter and receiver along with phase corrections. Since multiple receivers can simultaneously receive the reflection when a transmitter emits a signal and then efficiently create virtual subarrays obtained by coprime measurements, the proposed radar array system can further reduce the number of measurements (antennas) and the amount of data compared with the existing schemes. Our proposal is demonstrated through simulations and experiments, and the results indicate that the proposed radar array system is advantageous in implementation in terms of hardware design and data acquisition time.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"2 ","pages":"1036-1047"},"PeriodicalIF":0.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142524139","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}
Time-delay estimation (TDE) using ground penetrating radar (GPR) is of great importance in roadway surveys. The conventional GPR methods apply a uniform sampling strategy for TDE, which requires numerous frequency sampling points, leading to lengthy data acquisition time and large data storage, especially for ultra-wideband (UWB) radar. Moreover, detecting the overlapped backscattered echoes from the thin layer of roadways remains a challenge in TDE, due to the limited resolution of GPR and the characteristics of GPR signals. To address these issues, we derive a co-prime sampling strategy-based TDE for thin layers in roadway survey by exploiting off-grid sparse Bayesian learning (OGSBL), referred to co-prime-OGSBL. In our scheme, the sampling rate of GPR signals with a co-prime sampling strategy is greatly reduced compared with the uniform sampling, which therefore reduces the data acquisition burden and computational complexity. The estimation performance of time delays and thickness is also enhanced with OGSBL by utilizing radar pulse, co-prime sampling, and noncircularity of GPR signals. Both simulation and experimental results demonstrate the efficiency and accuracy of the proposed method in the estimation of time delays and thickness.
{"title":"Co-Prime Sampling-Based Time-Delay Estimation for Roadway Survey by Ground Penetrating Radar via Off-Grid Sparse Bayesian Learning","authors":"Jingjing Pan;Huimin Pan;Meng Sun;Yide Wang;Vincent Baltazart;Xudong Dong;Jun Zhao;Xiaofei Zhang;Hing Cheung So","doi":"10.1109/TRS.2024.3467993","DOIUrl":"https://doi.org/10.1109/TRS.2024.3467993","url":null,"abstract":"Time-delay estimation (TDE) using ground penetrating radar (GPR) is of great importance in roadway surveys. The conventional GPR methods apply a uniform sampling strategy for TDE, which requires numerous frequency sampling points, leading to lengthy data acquisition time and large data storage, especially for ultra-wideband (UWB) radar. Moreover, detecting the overlapped backscattered echoes from the thin layer of roadways remains a challenge in TDE, due to the limited resolution of GPR and the characteristics of GPR signals. To address these issues, we derive a co-prime sampling strategy-based TDE for thin layers in roadway survey by exploiting off-grid sparse Bayesian learning (OGSBL), referred to co-prime-OGSBL. In our scheme, the sampling rate of GPR signals with a co-prime sampling strategy is greatly reduced compared with the uniform sampling, which therefore reduces the data acquisition burden and computational complexity. The estimation performance of time delays and thickness is also enhanced with OGSBL by utilizing radar pulse, co-prime sampling, and noncircularity of GPR signals. Both simulation and experimental results demonstrate the efficiency and accuracy of the proposed method in the estimation of time delays and thickness.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"2 ","pages":"966-978"},"PeriodicalIF":0.0,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142397287","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 novel constant false alarm rate (CFAR) compressed detection approach for human detection using the impulse radio ultrawideband (IR-UWB) radar. The associated Xampling scheme operates way below the Nyquist limit and is designed to minimize the sensing matrix coherence (SMC), without increasing the implementation complexity. The proposed signal-processing architecture aims to detect both moving and stationary people in the framework of heavy-cluttered use cases, such as smart factory indoor environments. To address this challenge, we not only rely on standard radar signal processing, including moving target indicator (MTI) filtering, noise whitening, and Doppler focusing (DF), but also introduce two new algorithms for joint sparse reconstruction (SR) and CFAR detection, in fast-time and range-Doppler domains, respectively. We propose a specific detection statistic, which is proven to be appropriate for both algorithms, its distribution being identified and then validated by standard goodness-of-fit tests. Moreover, it enables reducing the CFAR scheme complexity, since the associated detection threshold is invariant to the noise power, thus making unnecessary its estimation. The proposed approach is finally validated using both simulated and experimentally measured data in an Industry 4.0 indoor environment, for several canonical scenarios. The effectiveness of our CFAR compressed detection algorithms for human detection is thus fully demonstrated, and their performance is assessed and compared to that obtained by signal processing at the Nyquist sampling rate.
{"title":"CFAR Compressed Detection in Heavy-Cluttered Indoor Environments Using IR-UWB Radar: New Experimentally Supported Results","authors":"Zaynab Baydoun;Roua Youssef;Emanuel Radoi;Stéphane Azou;Tina Yaacoub","doi":"10.1109/TRS.2024.3467549","DOIUrl":"https://doi.org/10.1109/TRS.2024.3467549","url":null,"abstract":"This article presents a novel constant false alarm rate (CFAR) compressed detection approach for human detection using the impulse radio ultrawideband (IR-UWB) radar. The associated Xampling scheme operates way below the Nyquist limit and is designed to minimize the sensing matrix coherence (SMC), without increasing the implementation complexity. The proposed signal-processing architecture aims to detect both moving and stationary people in the framework of heavy-cluttered use cases, such as smart factory indoor environments. To address this challenge, we not only rely on standard radar signal processing, including moving target indicator (MTI) filtering, noise whitening, and Doppler focusing (DF), but also introduce two new algorithms for joint sparse reconstruction (SR) and CFAR detection, in fast-time and range-Doppler domains, respectively. We propose a specific detection statistic, which is proven to be appropriate for both algorithms, its distribution being identified and then validated by standard goodness-of-fit tests. Moreover, it enables reducing the CFAR scheme complexity, since the associated detection threshold is invariant to the noise power, thus making unnecessary its estimation. The proposed approach is finally validated using both simulated and experimentally measured data in an Industry 4.0 indoor environment, for several canonical scenarios. The effectiveness of our CFAR compressed detection algorithms for human detection is thus fully demonstrated, and their performance is assessed and compared to that obtained by signal processing at the Nyquist sampling rate.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"2 ","pages":"991-1006"},"PeriodicalIF":0.0,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142397286","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 : 2024-09-23DOI: 10.1109/TRS.2024.3466134
Yuan-Peng Zhang;Zhi-Hao Wang;Tai-Yang Liu;Yan Xie;Ying Luo
Heterogeneous radar network systems can provide multiband and multiangle information about targets, enhancing the ability to recognize space targets. This article proposes a space target recognition method based on a bidirectional gated recurrent unit (BiGRU)-Transformer and dual graph fusion (BiGT-DGF) network. Through a temporal information extraction subnetwork, the BiGRU and Transformer are used to dynamically model a radar cross section (RCS) time series under multiple bands and angles, effectively exploiting both the local and global temporal dependencies. Through a spatial information extraction subnetwork, which integrates predefined graphs with self-adaptive graphs, the spatial dependencies between various radars are dynamically and adaptively captured. On this basis, the prediction output layer utilizes the spatiotemporal information extracted by the above two subnetworks to effectively recognize space targets. The experimental results show that the proposed method can reliably recognize space targets even under low signal-to-noise ratios (SNRs) and low pulse repetition frequencies.
{"title":"Space Target Recognition Based on Radar Network Systems With BiGRU-Transformer and Dual Graph Fusion Network","authors":"Yuan-Peng Zhang;Zhi-Hao Wang;Tai-Yang Liu;Yan Xie;Ying Luo","doi":"10.1109/TRS.2024.3466134","DOIUrl":"https://doi.org/10.1109/TRS.2024.3466134","url":null,"abstract":"Heterogeneous radar network systems can provide multiband and multiangle information about targets, enhancing the ability to recognize space targets. This article proposes a space target recognition method based on a bidirectional gated recurrent unit (BiGRU)-Transformer and dual graph fusion (BiGT-DGF) network. Through a temporal information extraction subnetwork, the BiGRU and Transformer are used to dynamically model a radar cross section (RCS) time series under multiple bands and angles, effectively exploiting both the local and global temporal dependencies. Through a spatial information extraction subnetwork, which integrates predefined graphs with self-adaptive graphs, the spatial dependencies between various radars are dynamically and adaptively captured. On this basis, the prediction output layer utilizes the spatiotemporal information extracted by the above two subnetworks to effectively recognize space targets. The experimental results show that the proposed method can reliably recognize space targets even under low signal-to-noise ratios (SNRs) and low pulse repetition frequencies.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"2 ","pages":"950-965"},"PeriodicalIF":0.0,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368557","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}
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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：