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/RadarConf2351548.2023.10149693
Mingcheng Fu, Zhi Zheng, Yizhen Jia, Bang Huang, Wen-qin Wang
In this paper, we devise a novel cylindrical conformal array, termed cylindrical distributed coprime conformal array (CDCCA), for two-dimensional (2-D) direction-of-arrival (DOA) and polarization estimation. The proposed CDCCA avoids the lag redundancies between two adjacent linear subarrays of cylindrical conformal array, and increases the unique lags number in its difference coarray. Moreover, it provides a larger array aperture than the exiting cylindrical conformal arrays under the same number of sensors. Therefore, the CDCCA configuration can resolve a larger number of sources and provide a higher estimation accuracy. Numerical results demonstrate its superiority in comparison to several existing conformal arrays.
{"title":"Cylindrical Distributed Coprime Conformal Array for 2-D DOA and Polarization Estimation","authors":"Mingcheng Fu, Zhi Zheng, Yizhen Jia, Bang Huang, Wen-qin Wang","doi":"10.1109/RadarConf2351548.2023.10149693","DOIUrl":"https://doi.org/10.1109/RadarConf2351548.2023.10149693","url":null,"abstract":"In this paper, we devise a novel cylindrical conformal array, termed cylindrical distributed coprime conformal array (CDCCA), for two-dimensional (2-D) direction-of-arrival (DOA) and polarization estimation. The proposed CDCCA avoids the lag redundancies between two adjacent linear subarrays of cylindrical conformal array, and increases the unique lags number in its difference coarray. Moreover, it provides a larger array aperture than the exiting cylindrical conformal arrays under the same number of sensors. Therefore, the CDCCA configuration can resolve a larger number of sources and provide a higher estimation accuracy. Numerical results demonstrate its superiority in comparison to several existing conformal arrays.","PeriodicalId":168311,"journal":{"name":"2023 IEEE Radar Conference (RadarConf23)","volume":"5 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":"134513392","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/RadarConf2351548.2023.10149733
Nazila Karimian Sichani, Moein Ahmadi, E. Raei, M. Alaee-Kerahroodi, B. M. R., E. Mehrshahi, Seyyed Ali Ghorashi
The emerging 4D-imaging automotive MIMO radar sensors necessitate the selection of appropriate transmit wave-forms, which should be separable on the receive side in addition to having low auto-correlation sidelobes. TDM, FDM, DDM, and inter-chirp CDM approaches have traditionally been proposed for FMCW radar sensors to ensure the orthogonality of the transmit signals. However, as the number of transmit antennas increases, each of the aforementioned approaches suffers from some drawbacks, which are described in this paper. PMCW radars, on the other hand, can be considered to be more costly to implement, have been proposed to provide better performance and allow for the use of waveform optimization techniques. In this context, we use a block gradient descent approach to design a waveform set for MIMO-PMCW that is optimized based on weighted integrated sidelobe level in this paper, and we show that the proposed waveform outperforms conventional MIMO-FMCW approaches by performing comparative simulations.
{"title":"Waveform Selection for FMCW and PMCW 4D-Imaging Automotive Radar Sensors","authors":"Nazila Karimian Sichani, Moein Ahmadi, E. Raei, M. Alaee-Kerahroodi, B. M. R., E. Mehrshahi, Seyyed Ali Ghorashi","doi":"10.1109/RadarConf2351548.2023.10149733","DOIUrl":"https://doi.org/10.1109/RadarConf2351548.2023.10149733","url":null,"abstract":"The emerging 4D-imaging automotive MIMO radar sensors necessitate the selection of appropriate transmit wave-forms, which should be separable on the receive side in addition to having low auto-correlation sidelobes. TDM, FDM, DDM, and inter-chirp CDM approaches have traditionally been proposed for FMCW radar sensors to ensure the orthogonality of the transmit signals. However, as the number of transmit antennas increases, each of the aforementioned approaches suffers from some drawbacks, which are described in this paper. PMCW radars, on the other hand, can be considered to be more costly to implement, have been proposed to provide better performance and allow for the use of waveform optimization techniques. In this context, we use a block gradient descent approach to design a waveform set for MIMO-PMCW that is optimized based on weighted integrated sidelobe level in this paper, and we show that the proposed waveform outperforms conventional MIMO-FMCW approaches by performing comparative simulations.","PeriodicalId":168311,"journal":{"name":"2023 IEEE Radar Conference (RadarConf23)","volume":"28 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":"131787634","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/RadarConf2351548.2023.10149773
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/RadarConf2351548.2023.10149621
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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