Pub Date : 2023-05-01DOI: 10.1109/RadarConf2351548.2023.10149790
B. Knoedler, Martina Broetje, Christian Steffes, W. Koch
Based on past research results, passive radars are generally considered a class of sensor systems well suited for the detection and tracking of small UAVs. When applying conventional detection and tracking methods (Detect-then-Track), however, several challenges arise. Target reflections may not be detected due to low signal-to-noise ratio. Further, multiple target-dependent measurements, due to non radar optimized waveforms and target movement, make data association more demanding. Track-before-Detect methods represent an alternative to such processing schemes, where a classical threshold detector is avoided and the whole target reflection is modeled in the measurement space. This work gives an overview of the fundamentals and differences of both target tracking concepts, before evaluating experimental data using two cooperative drones in a GSM passive radar scenario.
{"title":"Detecting and Tracking Multiple Small UAV Using Passive Radar","authors":"B. Knoedler, Martina Broetje, Christian Steffes, W. Koch","doi":"10.1109/RadarConf2351548.2023.10149790","DOIUrl":"https://doi.org/10.1109/RadarConf2351548.2023.10149790","url":null,"abstract":"Based on past research results, passive radars are generally considered a class of sensor systems well suited for the detection and tracking of small UAVs. When applying conventional detection and tracking methods (Detect-then-Track), however, several challenges arise. Target reflections may not be detected due to low signal-to-noise ratio. Further, multiple target-dependent measurements, due to non radar optimized waveforms and target movement, make data association more demanding. Track-before-Detect methods represent an alternative to such processing schemes, where a classical threshold detector is avoided and the whole target reflection is modeled in the measurement space. This work gives an overview of the fundamentals and differences of both target tracking concepts, before evaluating experimental data using two cooperative drones in a GSM passive radar scenario.","PeriodicalId":168311,"journal":{"name":"2023 IEEE Radar Conference (RadarConf23)","volume":"79 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":"122861154","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/RadarConf2351548.2023.10149652
Wenyan Wei, Yinsheng Wei
It has been shown in traditional single antenna radar that designing transmit waveform with an ambiguity function that exhibits low values in specific range-Doppler bins can enhance target detection performance in the clutter from these bins. This letter extends this idea to the case of multiple-input multiple-output (MIMO) radars and deals with the design of a unimodular waveform set with a desired MIMO radar range-Doppler ambiguity function. The phase-coded sequences are generated by minimizing a unified metric that can represent the weighted integrated sidelobe level (WISL) and the peak sidelobe level (PSL) of the local ambiguity function by choosing different parameters. The resulting highly nonlinear optimization problem is solved by the limited-memory Broyden- Fletcher-Goldfarb-Shanno (L-BFGS) algorithm. Numerical results demonstrate the performance of the proposed method.
{"title":"Unimodular Sequence Set Design for MIMO Radar Ambiguity Function Shaping","authors":"Wenyan Wei, Yinsheng Wei","doi":"10.1109/RadarConf2351548.2023.10149652","DOIUrl":"https://doi.org/10.1109/RadarConf2351548.2023.10149652","url":null,"abstract":"It has been shown in traditional single antenna radar that designing transmit waveform with an ambiguity function that exhibits low values in specific range-Doppler bins can enhance target detection performance in the clutter from these bins. This letter extends this idea to the case of multiple-input multiple-output (MIMO) radars and deals with the design of a unimodular waveform set with a desired MIMO radar range-Doppler ambiguity function. The phase-coded sequences are generated by minimizing a unified metric that can represent the weighted integrated sidelobe level (WISL) and the peak sidelobe level (PSL) of the local ambiguity function by choosing different parameters. The resulting highly nonlinear optimization problem is solved by the limited-memory Broyden- Fletcher-Goldfarb-Shanno (L-BFGS) algorithm. Numerical results demonstrate the performance of the proposed method.","PeriodicalId":168311,"journal":{"name":"2023 IEEE Radar Conference (RadarConf23)","volume":"71 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":"126980889","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/RadarConf2351548.2023.10149713
Ashwin Bhobani Baral, Bhaskar Raj Upadhyay, M. Torlak
The mutual interference between automotive radar sensors is inevitable due to their increasing demand in automotive applications. To reliably estimate the target parameters, this interference needs to be detected and mitigated. This paper proposes a two-stage approach for suppressing the mutual interference between frequency modulated continuous wave (FMCW) radars. In the first stage, the signals corresponding to the strong interference components or targets are separated using the singular value decomposition (SVD) technique across the spatial domain. Following this, each separated signal at each receive channel is further decomposed into different frequency components using various mode decomposition techniques such as empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), and variational mode decomposition (VMD) methods. The performance comparison of these different mode decomposition approaches with our proposed idea is presented through a simulation and a real experiment.
{"title":"Automotive Radar Interference Mitigation Using Two-Stage Signal Decomposition Approach","authors":"Ashwin Bhobani Baral, Bhaskar Raj Upadhyay, M. Torlak","doi":"10.1109/RadarConf2351548.2023.10149713","DOIUrl":"https://doi.org/10.1109/RadarConf2351548.2023.10149713","url":null,"abstract":"The mutual interference between automotive radar sensors is inevitable due to their increasing demand in automotive applications. To reliably estimate the target parameters, this interference needs to be detected and mitigated. This paper proposes a two-stage approach for suppressing the mutual interference between frequency modulated continuous wave (FMCW) radars. In the first stage, the signals corresponding to the strong interference components or targets are separated using the singular value decomposition (SVD) technique across the spatial domain. Following this, each separated signal at each receive channel is further decomposed into different frequency components using various mode decomposition techniques such as empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), and variational mode decomposition (VMD) methods. The performance comparison of these different mode decomposition approaches with our proposed idea is presented through a simulation and a real experiment.","PeriodicalId":168311,"journal":{"name":"2023 IEEE Radar Conference (RadarConf23)","volume":"9 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":"133341532","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/RadarConf2351548.2023.10149651
Adnan Albaba, M. Bauduin, Hichem Sahli, A. Bourdoux
In this paper, the three-dimensional (3-D) imaging problem of monostatic forward-looking synthetic aperture radar (FL-SAR) is analyzed. A 3-D guided-and-decimated backprojection (3-D GDBP) algorithm is proposed for reducing the computational complexity of 3-D FL-SAR image reconstruction. This is done by combining range and Doppler processing together with decimation along the slow-time samples and backprojection along the fast-time samples. In addition, the geometry and frequency-modulated continuous wave (FMCW) signal model for the 3-D FL-SAR problem are presented. Finally, the performance of the proposed method is tested and compared against the 3-D decimated backprojection algorithm.
{"title":"Low-Complexity Forward-Looking Volumetric SAR for High Resolution 3-D Radar Imaging","authors":"Adnan Albaba, M. Bauduin, Hichem Sahli, A. Bourdoux","doi":"10.1109/RadarConf2351548.2023.10149651","DOIUrl":"https://doi.org/10.1109/RadarConf2351548.2023.10149651","url":null,"abstract":"In this paper, the three-dimensional (3-D) imaging problem of monostatic forward-looking synthetic aperture radar (FL-SAR) is analyzed. A 3-D guided-and-decimated backprojection (3-D GDBP) algorithm is proposed for reducing the computational complexity of 3-D FL-SAR image reconstruction. This is done by combining range and Doppler processing together with decimation along the slow-time samples and backprojection along the fast-time samples. In addition, the geometry and frequency-modulated continuous wave (FMCW) signal model for the 3-D FL-SAR problem are presented. Finally, the performance of the proposed method is tested and compared against the 3-D decimated backprojection algorithm.","PeriodicalId":168311,"journal":{"name":"2023 IEEE Radar Conference (RadarConf23)","volume":"12 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":"134008639","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/RadarConf2351548.2023.10149791
J. Rodriguez, F. Colone, P. Lombardo
In this paper, we present an experimental evaluation of recently proposed Supervised Reciprocal Filter approaches for the compression of OFDM-radar signals. The range-Doppler map is usually evaluated using a suboptimal batches algorithm, after fragmenting the signal in batches with length equal to the OFDM symbol. Using “OFDM fragmentation” requires symbol synchronization and sets constraints on the non-ambiguous Range-Doppler area of targets that can be detected with limited Signal-to-Noise Ratio (SNR) loss. Supervised Reciprocal Filters have been recently proposed to operate with batches of longer lengths than the OFDM symbol without requiring any synchronization. In this paper we extend the study to include the case of batches equal to a fraction of the OFDM symbol, which provides higher flexibility to adapt the processor to the range-Doppler scenario of interest. These filters have been shown to contain the large SNR losses obtained with a direct application of the Reciprocal Filter (RF) with the non-OFDM fragmentation. Moreover, they have been shown theoretically to preserve the benefits of the RF over the Matched Filter (MF) against the clutter-limited scenarios. To assess the performance of the Supervised Filter against a real scenario, an acquisition campaign has been carried out using the Sapienza experimental passive radar along the coast north of Rome, against a maritime traffic scenario, including non-cooperative vessels, as well as a cooperating small boat equipped with differential GPS positioning registration tools. The effectiveness of the proposed approaches is validated by applying them to experimental data from a PBR application exploiting DVB-T transmissions.
{"title":"Experimental evaluation of Supervised Reciprocal Filter Strategies for OFDM-radar signal processing","authors":"J. Rodriguez, F. Colone, P. Lombardo","doi":"10.1109/RadarConf2351548.2023.10149791","DOIUrl":"https://doi.org/10.1109/RadarConf2351548.2023.10149791","url":null,"abstract":"In this paper, we present an experimental evaluation of recently proposed Supervised Reciprocal Filter approaches for the compression of OFDM-radar signals. The range-Doppler map is usually evaluated using a suboptimal batches algorithm, after fragmenting the signal in batches with length equal to the OFDM symbol. Using “OFDM fragmentation” requires symbol synchronization and sets constraints on the non-ambiguous Range-Doppler area of targets that can be detected with limited Signal-to-Noise Ratio (SNR) loss. Supervised Reciprocal Filters have been recently proposed to operate with batches of longer lengths than the OFDM symbol without requiring any synchronization. In this paper we extend the study to include the case of batches equal to a fraction of the OFDM symbol, which provides higher flexibility to adapt the processor to the range-Doppler scenario of interest. These filters have been shown to contain the large SNR losses obtained with a direct application of the Reciprocal Filter (RF) with the non-OFDM fragmentation. Moreover, they have been shown theoretically to preserve the benefits of the RF over the Matched Filter (MF) against the clutter-limited scenarios. To assess the performance of the Supervised Filter against a real scenario, an acquisition campaign has been carried out using the Sapienza experimental passive radar along the coast north of Rome, against a maritime traffic scenario, including non-cooperative vessels, as well as a cooperating small boat equipped with differential GPS positioning registration tools. The effectiveness of the proposed approaches is validated by applying them to experimental data from a PBR application exploiting DVB-T transmissions.","PeriodicalId":168311,"journal":{"name":"2023 IEEE Radar Conference (RadarConf23)","volume":"42 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":"133041604","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/RadarConf2351548.2023.10149764
David Luong, Ian Lam, B. Balaji, S. Rajan
In previous work, it was shown that a noise radars have two signal-to-noise ratios (SNRs) associated with them: one for the receive signal and another for the signal retained within for matched filtering. However, these two SNRs can be combined into a single correlation coefficient which can be easily be used for performance prediction. Unlike SNR, this correlation coefficient can be estimated directly from radar detection data. This work presents experimental verification of the theoretical relationship between the SNRs of a noise radar and the correlation coefficient, showing that it holds for a wide range of transmit powers.
{"title":"Correlation Coefficient vs. Transmit Power for an Experimental Noise Radar","authors":"David Luong, Ian Lam, B. Balaji, S. Rajan","doi":"10.1109/RadarConf2351548.2023.10149764","DOIUrl":"https://doi.org/10.1109/RadarConf2351548.2023.10149764","url":null,"abstract":"In previous work, it was shown that a noise radars have two signal-to-noise ratios (SNRs) associated with them: one for the receive signal and another for the signal retained within for matched filtering. However, these two SNRs can be combined into a single correlation coefficient which can be easily be used for performance prediction. Unlike SNR, this correlation coefficient can be estimated directly from radar detection data. This work presents experimental verification of the theoretical relationship between the SNRs of a noise radar and the correlation coefficient, showing that it holds for a wide range of transmit powers.","PeriodicalId":168311,"journal":{"name":"2023 IEEE Radar Conference (RadarConf23)","volume":"195 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114121150","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/RadarConf2351548.2023.10149670
Sunila Akbar, R. Adve, Z. Ding, P. Moo
A radar resource management module in a cognitive multifunction radar manages the resources by first prioritizing and then scheduling the tasks. Apart from scheduling the tasks, the task scheduler of a cognitive radar requires the scheduling to be adaptable to the changing environment. We formulate a gen-eral model for the distributions of task parameters, specifically, task priorities and delay tolerance, to ensure priority-based task scheduling. We develop the use of transfer learning (TL) within a deep reinforcement learning (DRL) framework to address the challenge of adaptability to a varying environment. Our approach builds on using a Monte Carlo Tree Search (MCTS) aided by a deep neural network (DNN). We show that TL allows accelerated training by transferring the policy learned by training the D NN-based MCTS on initial parameter distribution (environment) to the policy required for a new environment. Our results show that the high priority tasks are least delayed and dropped with the new formulation, whereas TL ensures the respective adaptation to the dynamic environment.
{"title":"Priority-based Task Scheduling in Dynamic Environments for Cognitive MFR via Transfer DRL","authors":"Sunila Akbar, R. Adve, Z. Ding, P. Moo","doi":"10.1109/RadarConf2351548.2023.10149670","DOIUrl":"https://doi.org/10.1109/RadarConf2351548.2023.10149670","url":null,"abstract":"A radar resource management module in a cognitive multifunction radar manages the resources by first prioritizing and then scheduling the tasks. Apart from scheduling the tasks, the task scheduler of a cognitive radar requires the scheduling to be adaptable to the changing environment. We formulate a gen-eral model for the distributions of task parameters, specifically, task priorities and delay tolerance, to ensure priority-based task scheduling. We develop the use of transfer learning (TL) within a deep reinforcement learning (DRL) framework to address the challenge of adaptability to a varying environment. Our approach builds on using a Monte Carlo Tree Search (MCTS) aided by a deep neural network (DNN). We show that TL allows accelerated training by transferring the policy learned by training the D NN-based MCTS on initial parameter distribution (environment) to the policy required for a new environment. Our results show that the high priority tasks are least delayed and dropped with the new formulation, whereas TL ensures the respective adaptation to the dynamic environment.","PeriodicalId":168311,"journal":{"name":"2023 IEEE Radar Conference (RadarConf23)","volume":"3 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113941389","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/RadarConf2351548.2023.10149595
Jinglu He, Wenlong Chang, Fuping Wang, Y. Liu, Chenglu Sun, Yinghua Li
As one of crucial remote sensing applications, ship classification using synthetic aperture radar (SAR) images has increasingly been studied in modern maritime surveillance. Nowadays, the prevailing classification paradigm for SAR ship targets is to utilize the deep network models, which presents superior performance over the traditional handcrafted feature driven methods. Of which the SAR ship classification method using densely connected convolutional neural networks (CNNs) is among the state-of-the-art. However, the general CNNs cannot fully explore the SAR ship feature representations, which limits its potentials for better classification performance. In this paper, we propose a novel multi-scale framework for the CNNs to further improve the ship classification performance with dual-polarization SAR images. Particularly, the convolutional feature maps from different spatial scales are fused to acquire multi-scale global representations of the dual-polarization SAR images, which are finally integrated by the group bilinear pooling operation in the classification layer and will further be processed by multiple classifiers for better network training. Extensive experiments have proved that the proposed method can improve the robustness and classification performance against the state-of-the-art algorithms on the OpenSARShip datasets.
{"title":"Multi-Scale Dense Networks for Ship Classification Using Dual-Polarization SAR Images","authors":"Jinglu He, Wenlong Chang, Fuping Wang, Y. Liu, Chenglu Sun, Yinghua Li","doi":"10.1109/RadarConf2351548.2023.10149595","DOIUrl":"https://doi.org/10.1109/RadarConf2351548.2023.10149595","url":null,"abstract":"As one of crucial remote sensing applications, ship classification using synthetic aperture radar (SAR) images has increasingly been studied in modern maritime surveillance. Nowadays, the prevailing classification paradigm for SAR ship targets is to utilize the deep network models, which presents superior performance over the traditional handcrafted feature driven methods. Of which the SAR ship classification method using densely connected convolutional neural networks (CNNs) is among the state-of-the-art. However, the general CNNs cannot fully explore the SAR ship feature representations, which limits its potentials for better classification performance. In this paper, we propose a novel multi-scale framework for the CNNs to further improve the ship classification performance with dual-polarization SAR images. Particularly, the convolutional feature maps from different spatial scales are fused to acquire multi-scale global representations of the dual-polarization SAR images, which are finally integrated by the group bilinear pooling operation in the classification layer and will further be processed by multiple classifiers for better network training. Extensive experiments have proved that the proposed method can improve the robustness and classification performance against the state-of-the-art algorithms on the OpenSARShip datasets.","PeriodicalId":168311,"journal":{"name":"2023 IEEE Radar Conference (RadarConf23)","volume":"253 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":"117308405","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/RadarConf2351548.2023.10149621
Bahozhoni White, Matthew B. Heintzelman, S. Blunt
Even for a fixed time-bandwidth product there are infinite possible spectrally-shaped random FM (RFM) waveforms one could generate due to their being phase-continuous. Moreover, certain RFM classes rely on an imposed basis-like structure scaled by underlying parameters that can be optimized (e.g. gradient-descent and greedy search have been demonstrated). Because these structures must include oversampling with respect to 3-dB bandwidth to account for sufficient spectral roll-off (necessary to be physically realizable in hardware), they are not true bases (i.e. not square). Therefore, any individual structure cannot represent all possible waveforms, with the waveforms generated by a given structure tending to possess similar attributes. Here we examine these attributes for some particular design structures, which may inform their selection for given radar applications.
{"title":"Alternative “Bases” for Gradient-Based Optimization of Parameterized FM Radar Waveforms","authors":"Bahozhoni White, Matthew B. Heintzelman, S. Blunt","doi":"10.1109/RadarConf2351548.2023.10149621","DOIUrl":"https://doi.org/10.1109/RadarConf2351548.2023.10149621","url":null,"abstract":"Even for a fixed time-bandwidth product there are infinite possible spectrally-shaped random FM (RFM) waveforms one could generate due to their being phase-continuous. Moreover, certain RFM classes rely on an imposed basis-like structure scaled by underlying parameters that can be optimized (e.g. gradient-descent and greedy search have been demonstrated). Because these structures must include oversampling with respect to 3-dB bandwidth to account for sufficient spectral roll-off (necessary to be physically realizable in hardware), they are not true bases (i.e. not square). Therefore, any individual structure cannot represent all possible waveforms, with the waveforms generated by a given structure tending to possess similar attributes. Here we examine these attributes for some particular design structures, which may inform their selection for given radar applications.","PeriodicalId":168311,"journal":{"name":"2023 IEEE Radar Conference (RadarConf23)","volume":"162 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":"115427600","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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