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/RadarConf2351548.2023.10149612
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/RadarConf2351548.2023.10149625
Nicolai Kern, Julian Aguilar, Pirmin Schoeder, C. Waldschmidt
A key element for the interaction between pedestrians and autonomous vehicles is the automated recognition of traffic and communication gestures. Gestures help vehicles to resolve critical or ambiguous situations. Detecting gestures with radar sensors is advantageous with respect to environmental conditions and lighting. However, the collection of a radar dataset that covers the wide range of variations in automotive scenarios comes at high cost and effort. On the other side, datasets with limited variations lead to reduced recognition accuracy or even complete failure in new scenarios. Hence, this paper analyzes the impact that deficiencies of traffic gesture datasets can have on the accuracy and investigates mitigation strategies based on the augmentation by simulated, variation-rich radar data. It is shown that by augmentation the robustness of a convolutional neural network (CNN)-based classifier against variations not covered by the training data is significantly improved. As a key result, both complete failure of the classifier and strongly decreased classification accuracy are avoided.
{"title":"Improving the Robustness of Automotive Gesture Recognition by Diversified Simulation Datasets","authors":"Nicolai Kern, Julian Aguilar, Pirmin Schoeder, C. Waldschmidt","doi":"10.1109/RadarConf2351548.2023.10149625","DOIUrl":"https://doi.org/10.1109/RadarConf2351548.2023.10149625","url":null,"abstract":"A key element for the interaction between pedestrians and autonomous vehicles is the automated recognition of traffic and communication gestures. Gestures help vehicles to resolve critical or ambiguous situations. Detecting gestures with radar sensors is advantageous with respect to environmental conditions and lighting. However, the collection of a radar dataset that covers the wide range of variations in automotive scenarios comes at high cost and effort. On the other side, datasets with limited variations lead to reduced recognition accuracy or even complete failure in new scenarios. Hence, this paper analyzes the impact that deficiencies of traffic gesture datasets can have on the accuracy and investigates mitigation strategies based on the augmentation by simulated, variation-rich radar data. It is shown that by augmentation the robustness of a convolutional neural network (CNN)-based classifier against variations not covered by the training data is significantly improved. As a key result, both complete failure of the classifier and strongly decreased classification accuracy are avoided.","PeriodicalId":168311,"journal":{"name":"2023 IEEE Radar Conference (RadarConf23)","volume":"195 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":"125725318","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}
Pub Date : 2023-05-01DOI: 10.1109/RadarConf2351548.2023.10149639
Brandon Ravenscroft, Alfred Fontes, Patrick M. McCormick, S. Blunt, Cameron H. Musgrove
Leveraging a recent method for spectrally-shaped random FM (RFM) waveform generation, in conjunction with a particular implementation of spread-spectrum signaling, a multi-user form of dual-function radar/communication (DFRC) is proposed that seeks to balance the disparate requirements of each function. Using a radar-amenable spread-spectrum multiple-access signaling scheme, receive dynamic range for sensing is preserved by exploiting high-dimensional (and thus separable) waveforms, which are specifically structured to convey encoded information in a manner that can be readily decoded at a communication receiver.
{"title":"Physically Realizable Multi-User Radar/Communications (MURC)","authors":"Brandon Ravenscroft, Alfred Fontes, Patrick M. McCormick, S. Blunt, Cameron H. Musgrove","doi":"10.1109/RadarConf2351548.2023.10149639","DOIUrl":"https://doi.org/10.1109/RadarConf2351548.2023.10149639","url":null,"abstract":"Leveraging a recent method for spectrally-shaped random FM (RFM) waveform generation, in conjunction with a particular implementation of spread-spectrum signaling, a multi-user form of dual-function radar/communication (DFRC) is proposed that seeks to balance the disparate requirements of each function. Using a radar-amenable spread-spectrum multiple-access signaling scheme, receive dynamic range for sensing is preserved by exploiting high-dimensional (and thus separable) waveforms, which are specifically structured to convey encoded information in a manner that can be readily decoded at a communication receiver.","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":"130535192","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/RadarConf2351548.2023.10149749
Grant Norrie, S. Paine
A joint Radar Communications testbed is presented. This testbed leverages the OFDM based DAB standard to generate Radcom signals. The extended DAB mode structure used to describe these signals was used as the basis on which the communications sub-systems were designed. Furthermore, a radar processing subsystem was developed to process the same signal. Finally a functional testbed was deployed and used to complete system integration tests thereby demonstrating the joint RadCom functionality.
{"title":"Design and Demonstration of an OFDM Based RadCom System","authors":"Grant Norrie, S. Paine","doi":"10.1109/RadarConf2351548.2023.10149749","DOIUrl":"https://doi.org/10.1109/RadarConf2351548.2023.10149749","url":null,"abstract":"A joint Radar Communications testbed is presented. This testbed leverages the OFDM based DAB standard to generate Radcom signals. The extended DAB mode structure used to describe these signals was used as the basis on which the communications sub-systems were designed. Furthermore, a radar processing subsystem was developed to process the same signal. Finally a functional testbed was deployed and used to complete system integration tests thereby demonstrating the joint RadCom functionality.","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":"130887889","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/RadarConf2351548.2023.10149773
M. Farshchian, Benjamin Cowen, I. Selesnick
Sea clutter consists of three components: a mean Doppler spectrum, persistent spikes, and discrete spikes, with a random degree of relative power for each component. We propose a non-linear optimization technique designed to decompose noisy sea clutter into these three components plus a noise component using sparsity inducing norms and linear time-invariant (LTI) filtering in various domains. This novel approach is proposed for non-stationary clutter because it avoids any quasistationarity assumptions, unlike the currently proposed state-of-the-art detectors [1]. The decomposition is applied to real South African sea clutter data provided by the Council for Scientific and Industrial Research (CSIR) [2]. We additionally propose a secondary classifier stage for post-processing of potential target detections from the decomposition, and discuss some features that assist in classification between targets and persistent spikes beyond amplitude. Several such extensions are discussed in the conclusion.
{"title":"Phenomenology Based Decomposition of Sea Clutter with a Secondary Target Classifier","authors":"M. Farshchian, Benjamin Cowen, I. Selesnick","doi":"10.1109/RadarConf2351548.2023.10149773","DOIUrl":"https://doi.org/10.1109/RadarConf2351548.2023.10149773","url":null,"abstract":"Sea clutter consists of three components: a mean Doppler spectrum, persistent spikes, and discrete spikes, with a random degree of relative power for each component. We propose a non-linear optimization technique designed to decompose noisy sea clutter into these three components plus a noise component using sparsity inducing norms and linear time-invariant (LTI) filtering in various domains. This novel approach is proposed for non-stationary clutter because it avoids any quasistationarity assumptions, unlike the currently proposed state-of-the-art detectors [1]. The decomposition is applied to real South African sea clutter data provided by the Council for Scientific and Industrial Research (CSIR) [2]. We additionally propose a secondary classifier stage for post-processing of potential target detections from the decomposition, and discuss some features that assist in classification between targets and persistent spikes beyond amplitude. Several such extensions are discussed in the conclusion.","PeriodicalId":168311,"journal":{"name":"2023 IEEE Radar Conference (RadarConf23)","volume":"24 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":"121301809","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}
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