Ship detection in synthetic aperture radar (SAR) images is a hot pot in the remote sensing (RS) field. However, most existing deep learning (DL)-based methods only focus on the single-polarization SAR ship detection without leveraging the rich dual-polarization SAR features, which poses a huge obstacle to the further model performance improvement. One problem for solution is how to fully excavate polarization characteristics using a convolution neural network (CNN). To address the above problem, we propose a novel group-wise feature fusion R-CNN (GWFF R-CNN) for dual-polarization SAR ship detection. Different from raw Faster R-CNN, GWFF R-CNN embeds a group-wise feature fusion module (GWFF module) into the subnetwork of Faster R-CNN, which enables group-wise feature fusion between polarization features and multi-scale ship features. Finally, the experiments on the dual-polarization SAR ship detection dataset (DSSDD) demonstrate that GWFF R-CNN can yield a ~4.1 F1 improvement and a ~2.9 average precision (AP) improvement, compared with Faster R-CNN.
{"title":"Group-Wise Feature Fusion R-CNN for Dual-Polarization SAR Ship Detection","authors":"Xiaowo Xu, Xiaoling Zhang, Tianjiao Zeng, Jun Shi, Zikang Shao, Tianwen Zhang","doi":"10.1109/RadarConf2351548.2023.10149675","DOIUrl":"https://doi.org/10.1109/RadarConf2351548.2023.10149675","url":null,"abstract":"Ship detection in synthetic aperture radar (SAR) images is a hot pot in the remote sensing (RS) field. However, most existing deep learning (DL)-based methods only focus on the single-polarization SAR ship detection without leveraging the rich dual-polarization SAR features, which poses a huge obstacle to the further model performance improvement. One problem for solution is how to fully excavate polarization characteristics using a convolution neural network (CNN). To address the above problem, we propose a novel group-wise feature fusion R-CNN (GWFF R-CNN) for dual-polarization SAR ship detection. Different from raw Faster R-CNN, GWFF R-CNN embeds a group-wise feature fusion module (GWFF module) into the subnetwork of Faster R-CNN, which enables group-wise feature fusion between polarization features and multi-scale ship features. Finally, the experiments on the dual-polarization SAR ship detection dataset (DSSDD) demonstrate that GWFF R-CNN can yield a ~4.1 F1 improvement and a ~2.9 average precision (AP) improvement, compared with Faster R-CNN.","PeriodicalId":168311,"journal":{"name":"2023 IEEE Radar Conference (RadarConf23)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117005727","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 : 2023-05-01DOI: 10.1109/RadarConf2351548.2023.10149728
S. Biswas, Benjamin Bartlett, J. Ball, A. Gurbuz
Advanced driver-assisted system (ADAS) typically includes sensors such as Radar, Lidar, or Camera to make vehicles aware of their surroundings. These ADAS systems are presented to a wide variety of situations in traffic, such as upcoming collisions, lane changes, intersections, sudden changes in speed, and other common instances of driving errors. One of the key barriers to automotive autonomy is the inability of self-driving cars to navigate unstructured environments, which typically do not have any traffic lights present or operational for directing traffic. In these circumstances, it is much more common for a person to be tasked with directing vehicles, either by signaling with an appropriate sign or via gesturing. The task of interpreting human body language and gestures by autonomous vehicles in traffic directing scenarios is a great challenge. In this study, we present a new dataset collected of traffic signaling motions using millimeter-wave (mmWave) radar, camera, Lidar and motion-capture system. The dataset is based on those utilized in the US traffic system. Initial classification results from Radar microDoppler (µ-D) signature analysis using basic Convolutional Neural Networks (CNN) demonstrates that deep learning can very accurately (around 92%) classify traffic signaling motions in automotive applications.
{"title":"Classification of Traffic Signaling Motion in Automotive Applications Using FMCW Radar","authors":"S. Biswas, Benjamin Bartlett, J. Ball, A. Gurbuz","doi":"10.1109/RadarConf2351548.2023.10149728","DOIUrl":"https://doi.org/10.1109/RadarConf2351548.2023.10149728","url":null,"abstract":"Advanced driver-assisted system (ADAS) typically includes sensors such as Radar, Lidar, or Camera to make vehicles aware of their surroundings. These ADAS systems are presented to a wide variety of situations in traffic, such as upcoming collisions, lane changes, intersections, sudden changes in speed, and other common instances of driving errors. One of the key barriers to automotive autonomy is the inability of self-driving cars to navigate unstructured environments, which typically do not have any traffic lights present or operational for directing traffic. In these circumstances, it is much more common for a person to be tasked with directing vehicles, either by signaling with an appropriate sign or via gesturing. The task of interpreting human body language and gestures by autonomous vehicles in traffic directing scenarios is a great challenge. In this study, we present a new dataset collected of traffic signaling motions using millimeter-wave (mmWave) radar, camera, Lidar and motion-capture system. The dataset is based on those utilized in the US traffic system. Initial classification results from Radar microDoppler (µ-D) signature analysis using basic Convolutional Neural Networks (CNN) demonstrates that deep learning can very accurately (around 92%) classify traffic signaling motions in automotive applications.","PeriodicalId":168311,"journal":{"name":"2023 IEEE Radar Conference (RadarConf23)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114936835","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 : 2023-05-01DOI: 10.1109/RadarConf2351548.2023.10149760
Y. Ivanenko, V. Vu, M. Pettersson
The THz frequency spectrum provides an opportunity to explore high-resolution synthetic-aperture-radar (SAR) short-range imaging that can be used for various applications. However, the performance of THz SAR imaging is sensitive to phase errors that can be caused by an insufficient amount of data samples for image formation and by path deviations that can be practically caused by SAR platform vibrations, changes in speed, changes in direction, and acceleration. To solve the former problem, an improved interpolation procedure for backprojection algorithms has been proposed. However, to make these algorithms efficient in handling the latter problem, an additional autofocusing is necessary. In this paper, we introduce an autofocusing procedure based on compressed sensing that is incorporated into the backprojection algorithm. The reconstruction is based on the following calculated parameters: windowed interpolation sinc kernel, and range distances between SAR platform and image pixels in a defined image plane. The proposed approach is tested on real data, which was acquired by the $2pi$ FMCW SAR system through outdoor SAR imaging.
{"title":"Autofocusing of THz SAR Images by Integrating Compressed Sensing into the Backprojection Process","authors":"Y. Ivanenko, V. Vu, M. Pettersson","doi":"10.1109/RadarConf2351548.2023.10149760","DOIUrl":"https://doi.org/10.1109/RadarConf2351548.2023.10149760","url":null,"abstract":"The THz frequency spectrum provides an opportunity to explore high-resolution synthetic-aperture-radar (SAR) short-range imaging that can be used for various applications. However, the performance of THz SAR imaging is sensitive to phase errors that can be caused by an insufficient amount of data samples for image formation and by path deviations that can be practically caused by SAR platform vibrations, changes in speed, changes in direction, and acceleration. To solve the former problem, an improved interpolation procedure for backprojection algorithms has been proposed. However, to make these algorithms efficient in handling the latter problem, an additional autofocusing is necessary. In this paper, we introduce an autofocusing procedure based on compressed sensing that is incorporated into the backprojection algorithm. The reconstruction is based on the following calculated parameters: windowed interpolation sinc kernel, and range distances between SAR platform and image pixels in a defined image plane. The proposed approach is tested on real data, which was acquired by the $2pi$ FMCW SAR system through outdoor SAR imaging.","PeriodicalId":168311,"journal":{"name":"2023 IEEE Radar Conference (RadarConf23)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121196044","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 : 2023-05-01DOI: 10.1109/RadarConf2351548.2023.10149653
Weitong Zhai, Xiangrong Wang, M. Greco, F. Gini
Integrated sensing and communication (ISAC) is a promising technique in vehicular transportation thanks to its substantial gains in size, cost, power consumption, electromag-netic compatibility and spectrum congestion. In this paper, we propose a reinforcement learning (RL) based ISAC system with a multi-input-multi-output (MIMO) automotive radar. The target sensing and downlink communication are separately performed by dividing the transmit antennas into two non-overlapping but interweaving subarrays. We first design a RL framework to adaptively allocate the proper number of transmit antennas for the two subarrays under any unknown environment. The training is performed in the metrics of Cramer-Rao Bound (CRB) of direction of arrival (DOA) estimation for sensing and receive signal-to-noise (SNR) for communications, respectively. We proceed to propose a co-design method to jointly optimize the configurations of the two subarrays to further enhance the sensing accuracy with a constrained communication quality. The resultant problem is converted into the convex form via convex relaxation. Simulations are provided to demonstrate the adaptability and effectiveness of the proposed RL based ISAC system under the unkown environment.
{"title":"Reinforcement Learning based Integrated Sensing and Communication for Automotive MIMO Radar","authors":"Weitong Zhai, Xiangrong Wang, M. Greco, F. Gini","doi":"10.1109/RadarConf2351548.2023.10149653","DOIUrl":"https://doi.org/10.1109/RadarConf2351548.2023.10149653","url":null,"abstract":"Integrated sensing and communication (ISAC) is a promising technique in vehicular transportation thanks to its substantial gains in size, cost, power consumption, electromag-netic compatibility and spectrum congestion. In this paper, we propose a reinforcement learning (RL) based ISAC system with a multi-input-multi-output (MIMO) automotive radar. The target sensing and downlink communication are separately performed by dividing the transmit antennas into two non-overlapping but interweaving subarrays. We first design a RL framework to adaptively allocate the proper number of transmit antennas for the two subarrays under any unknown environment. The training is performed in the metrics of Cramer-Rao Bound (CRB) of direction of arrival (DOA) estimation for sensing and receive signal-to-noise (SNR) for communications, respectively. We proceed to propose a co-design method to jointly optimize the configurations of the two subarrays to further enhance the sensing accuracy with a constrained communication quality. The resultant problem is converted into the convex form via convex relaxation. Simulations are provided to demonstrate the adaptability and effectiveness of the proposed RL based ISAC system under the unkown environment.","PeriodicalId":168311,"journal":{"name":"2023 IEEE Radar Conference (RadarConf23)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121202935","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 : 2023-05-01DOI: 10.1109/RadarConf2351548.2023.10149461
C. Thornton, R. Buehrer
When should an online reinforcement learning-based frequency agile cognitive radar be expected to outperform a rule-based adaptive waveform selection strategy? We seek insight regarding this question by examining a dynamic spectrum access scenario, in which the radar wishes to transmit in the widest unoccupied bandwidth during each pulse repetition interval. Online learning is compared to a fixed rule-based sense-and-avoid strategy. We show that given a simple Markov channel model, the problem can be examined analytically for simple cases via stochastic dominance. Additionally, we show that for more realistic channel assumptions, learning-based approaches demonstrate greater ability to generalize. However, for short time-horizon problems that are well-specified, we find that machine learning approaches may perform poorly due to the inherent limitation of convergence time. We draw conclusions as to when learning-based approaches are expected to be beneficial and provide guidelines for future study.
{"title":"When is Cognitive Radar Beneficial? Insights from Dynamic Spectrum Access","authors":"C. Thornton, R. Buehrer","doi":"10.1109/RadarConf2351548.2023.10149461","DOIUrl":"https://doi.org/10.1109/RadarConf2351548.2023.10149461","url":null,"abstract":"When should an online reinforcement learning-based frequency agile cognitive radar be expected to outperform a rule-based adaptive waveform selection strategy? We seek insight regarding this question by examining a dynamic spectrum access scenario, in which the radar wishes to transmit in the widest unoccupied bandwidth during each pulse repetition interval. Online learning is compared to a fixed rule-based sense-and-avoid strategy. We show that given a simple Markov channel model, the problem can be examined analytically for simple cases via stochastic dominance. Additionally, we show that for more realistic channel assumptions, learning-based approaches demonstrate greater ability to generalize. However, for short time-horizon problems that are well-specified, we find that machine learning approaches may perform poorly due to the inherent limitation of convergence time. We draw conclusions as to when learning-based approaches are expected to be beneficial and provide guidelines for future study.","PeriodicalId":168311,"journal":{"name":"2023 IEEE Radar Conference (RadarConf23)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124731993","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 : 2023-05-01DOI: 10.1109/RadarConf2351548.2023.10149576
P. Raju, D. B. Herr, J. Stiles
For adaptable pulse-agile radar systems, an optimal method to combine the responses from dissimilar transmit signals is sought. As the traditional method of matched filtering fails to provide sufficient performance in a pulse-agile regime, an iterative form of the MMSE estimator is presented to be the solution. By using the linear radar model and opting to process data within the temporal frequency domain, the implementation of the iterative MMSE estimator becomes computationally efficient. This method is compared with matched filtering, in both simulation and experimental data, and shown to produce a more accurate estimate of the scattering profile with finer range resolution and decreased correlation error.
{"title":"Efficient Iterative MMSE Range Profile Estimation","authors":"P. Raju, D. B. Herr, J. Stiles","doi":"10.1109/RadarConf2351548.2023.10149576","DOIUrl":"https://doi.org/10.1109/RadarConf2351548.2023.10149576","url":null,"abstract":"For adaptable pulse-agile radar systems, an optimal method to combine the responses from dissimilar transmit signals is sought. As the traditional method of matched filtering fails to provide sufficient performance in a pulse-agile regime, an iterative form of the MMSE estimator is presented to be the solution. By using the linear radar model and opting to process data within the temporal frequency domain, the implementation of the iterative MMSE estimator becomes computationally efficient. This method is compared with matched filtering, in both simulation and experimental data, and shown to produce a more accurate estimate of the scattering profile with finer range resolution and decreased correlation error.","PeriodicalId":168311,"journal":{"name":"2023 IEEE Radar Conference (RadarConf23)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121560457","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 : 2023-05-01DOI: 10.1109/RadarConf2351548.2023.10149704
Brian D. Carlton, J. Mcdaniel, J. Metcalf
The design and optimization of radar waveforms to possess minimal sidelobes has been an active area of research for decades. Here a new formulation of the trade space between the intrinsic resolution of a radar waveform and its sidelobe level is explored. Specifically, the tradeoff between main lobe resolution and sidelobe level is formally linked via the Dolph-Chebyshev window formulation. It is shown that the frequency-domain Dolph-Chebyshev formulation can be leveraged to generalize this tradeoff for waveform design. Further, the two-tone waveform (known to be optimal from a resolution perspective) and the Gaussian power spectral density waveform (known to be optimal from a sidelobe perspective) are shown to be special cases of this more generic expression. Finally, this new waveform design technique is combined with the pseudo-random optimized frequency modulation (PRO-FM) framework to produce physically realizable. constant modulus waveforms.
{"title":"Optimizing the Tradeoff Between Radar Waveform Resolution and Sidelobe Level Using a Dolph-Chebyshev Approach","authors":"Brian D. Carlton, J. Mcdaniel, J. Metcalf","doi":"10.1109/RadarConf2351548.2023.10149704","DOIUrl":"https://doi.org/10.1109/RadarConf2351548.2023.10149704","url":null,"abstract":"The design and optimization of radar waveforms to possess minimal sidelobes has been an active area of research for decades. Here a new formulation of the trade space between the intrinsic resolution of a radar waveform and its sidelobe level is explored. Specifically, the tradeoff between main lobe resolution and sidelobe level is formally linked via the Dolph-Chebyshev window formulation. It is shown that the frequency-domain Dolph-Chebyshev formulation can be leveraged to generalize this tradeoff for waveform design. Further, the two-tone waveform (known to be optimal from a resolution perspective) and the Gaussian power spectral density waveform (known to be optimal from a sidelobe perspective) are shown to be special cases of this more generic expression. Finally, this new waveform design technique is combined with the pseudo-random optimized frequency modulation (PRO-FM) framework to produce physically realizable. constant modulus waveforms.","PeriodicalId":168311,"journal":{"name":"2023 IEEE Radar Conference (RadarConf23)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117328344","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 : 2023-05-01DOI: 10.1109/RadarConf2351548.2023.10149612
Sean J. Kearney, S. Gurbuz
As radar technology becomes more readily available to researchers and users, it is thus being explored how to better process this data for real-time implementations. To process this radar data, the short time Fourier transform (STFT) has been implemented to then find the micro-Doppler spectrogram. When computing the STFT, there are parameters which can be adjusted to alter the size of the resulting micro-Doppler spectrogram. In this work, these parameters were adjusted to find the optimal representation of micro-Doppler radar returns of human activities, which were recorded using a 77 GHz Frequency Modulated Continuous Wave (FMCW) millimeter wave radar. To determine these optimal combinations, the resulting micro-Doppler spectrograms were used to train and test a Convolutional Autoencoder (CAE). The t-Distributed Stochastic Neighbor Embedding (t-SNE) and k-Nearest Neighbor Classification (kNN) were also utilized to find the nearest representations in a low-dimensional space of the spectrograms.
{"title":"Influence of Radar Signal Processing on Deep Learning-based Classification","authors":"Sean J. Kearney, S. Gurbuz","doi":"10.1109/RadarConf2351548.2023.10149612","DOIUrl":"https://doi.org/10.1109/RadarConf2351548.2023.10149612","url":null,"abstract":"As radar technology becomes more readily available to researchers and users, it is thus being explored how to better process this data for real-time implementations. To process this radar data, the short time Fourier transform (STFT) has been implemented to then find the micro-Doppler spectrogram. When computing the STFT, there are parameters which can be adjusted to alter the size of the resulting micro-Doppler spectrogram. In this work, these parameters were adjusted to find the optimal representation of micro-Doppler radar returns of human activities, which were recorded using a 77 GHz Frequency Modulated Continuous Wave (FMCW) millimeter wave radar. To determine these optimal combinations, the resulting micro-Doppler spectrograms were used to train and test a Convolutional Autoencoder (CAE). The t-Distributed Stochastic Neighbor Embedding (t-SNE) and k-Nearest Neighbor Classification (kNN) were also utilized to find the nearest representations in a low-dimensional space of the spectrograms.","PeriodicalId":168311,"journal":{"name":"2023 IEEE Radar Conference (RadarConf23)","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123966219","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 : 2023-05-01DOI: 10.1109/RadarConf2351548.2023.10149758
A. B. Carman, Changzhi Li
Indoor passive radar has gained traction as a method for measuring small-amplitude motions without requiring a cooperative signal to be transmitted by the sensor. Ubiquitous signals such as Wi-Fi and Bluetooth may be used as illuminators of opportunity in order to measure the motion of various targets. Both the direct, unmodulated signal as well as the Doppler-shifted signal are received at the radar and are used for down-conversion to baseband. Since there is no cooperative local oscillator used in passive radar, it is not currently possible to effectively extract both the $I$ and $Q$ channel data making null-point detection a returning problem. In this work, the null-point detection problem is analyzed theoretically to develop a simulation model for passive radar sensing. Using this model, an in-depth analysis is undertaken in order to determine the effectiveness of methods such as channel selection, frequency tuning, or multi-band/multi-static sensing in removing or mitigating the null-point detection problem. The results demonstrate that despite the presence of the null-point issue, it is possible to reduce its impact on motion detection and optimize the detection sensitivity.
{"title":"Null/Optimum Point Optimization for Indoor Passive Radar Motion Sensing","authors":"A. B. Carman, Changzhi Li","doi":"10.1109/RadarConf2351548.2023.10149758","DOIUrl":"https://doi.org/10.1109/RadarConf2351548.2023.10149758","url":null,"abstract":"Indoor passive radar has gained traction as a method for measuring small-amplitude motions without requiring a cooperative signal to be transmitted by the sensor. Ubiquitous signals such as Wi-Fi and Bluetooth may be used as illuminators of opportunity in order to measure the motion of various targets. Both the direct, unmodulated signal as well as the Doppler-shifted signal are received at the radar and are used for down-conversion to baseband. Since there is no cooperative local oscillator used in passive radar, it is not currently possible to effectively extract both the $I$ and $Q$ channel data making null-point detection a returning problem. In this work, the null-point detection problem is analyzed theoretically to develop a simulation model for passive radar sensing. Using this model, an in-depth analysis is undertaken in order to determine the effectiveness of methods such as channel selection, frequency tuning, or multi-band/multi-static sensing in removing or mitigating the null-point detection problem. The results demonstrate that despite the presence of the null-point issue, it is possible to reduce its impact on motion detection and optimize the detection sensitivity.","PeriodicalId":168311,"journal":{"name":"2023 IEEE Radar Conference (RadarConf23)","volume":"126 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124204047","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 : 2023-05-01DOI: 10.1109/RadarConf2351548.2023.10149614
Bang Huang, Wen-qin Wang, Weijian Liu, Mingcheng Fu, Zhi Zheng
This paper focuses on the detection of a point-like target in sample-starved environments with Gaussian interference, which includes strong main-lobe interference and weak thermal noise for frequency diverse array multiple-input multiple-output (FDA-MIMO) radar. At the design stage, the target signature is only partially known and assumed to lie in a known subspace. To solve the sample-starved problem, we adopt a reduced-dimension method to decrease the requirement of training data via pre-multiplying test and training data by a suitable matrix representing the signal subspace. Then, the generalized likelihood ratio test criterion is applied to come up with a reduced-dimension subspace detector. Numerical results validate the effectiveness of proposed detector.
{"title":"Reduced-dimension Subspace Detector Design for FDA-MIMO Radar in Sample-starved Scenarios","authors":"Bang Huang, Wen-qin Wang, Weijian Liu, Mingcheng Fu, Zhi Zheng","doi":"10.1109/RadarConf2351548.2023.10149614","DOIUrl":"https://doi.org/10.1109/RadarConf2351548.2023.10149614","url":null,"abstract":"This paper focuses on the detection of a point-like target in sample-starved environments with Gaussian interference, which includes strong main-lobe interference and weak thermal noise for frequency diverse array multiple-input multiple-output (FDA-MIMO) radar. At the design stage, the target signature is only partially known and assumed to lie in a known subspace. To solve the sample-starved problem, we adopt a reduced-dimension method to decrease the requirement of training data via pre-multiplying test and training data by a suitable matrix representing the signal subspace. Then, the generalized likelihood ratio test criterion is applied to come up with a reduced-dimension subspace detector. Numerical results validate the effectiveness of proposed detector.","PeriodicalId":168311,"journal":{"name":"2023 IEEE Radar Conference (RadarConf23)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128023524","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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