Pub Date : 2023-05-01DOI: 10.1109/RadarConf2351548.2023.10149754
Emirhan Ozmen, Y. Ozkazanc
In this work, we propose a new method which we call DeepASTC, for antenna scanning type classification in Electronic Warfare Systems. DeepASTC is a deep neural network composed of LSTMs. Amplitude patterns of the deinterleaved radar pulses are fed into our network, and the corresponding scanning type is automatically obtained. DeepASTC and the Multiclass Support Vector Machine (SVM) based classifier method are compared. It is observed that the proposed DeepASTC is able to achieve 93.8% correct classification rate on average, whereas the corresponding rate for the Multiclass SVM method is 86.3%. Conducted experiments show that, the proposed DeepASTC performs successfully on the synthetic data sets.
{"title":"DeepASTC:Antenna Scan Type Classification Using Deep Learning","authors":"Emirhan Ozmen, Y. Ozkazanc","doi":"10.1109/RadarConf2351548.2023.10149754","DOIUrl":"https://doi.org/10.1109/RadarConf2351548.2023.10149754","url":null,"abstract":"In this work, we propose a new method which we call DeepASTC, for antenna scanning type classification in Electronic Warfare Systems. DeepASTC is a deep neural network composed of LSTMs. Amplitude patterns of the deinterleaved radar pulses are fed into our network, and the corresponding scanning type is automatically obtained. DeepASTC and the Multiclass Support Vector Machine (SVM) based classifier method are compared. It is observed that the proposed DeepASTC is able to achieve 93.8% correct classification rate on average, whereas the corresponding rate for the Multiclass SVM method is 86.3%. Conducted experiments show that, the proposed DeepASTC performs successfully on the synthetic data sets.","PeriodicalId":168311,"journal":{"name":"2023 IEEE Radar Conference (RadarConf23)","volume":"83 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":"126183331","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/RadarConf2351548.2023.10149605
Victor Bursucianu, A. Amrhar, Jean-Marc Gagné, R. Landry
This paper presents the design and performance of a Direct RF Sampling Radar Altimeter based on the RFSoC from Xilinx. This architecture removes the mixing stage by sampling directly from the RF band of interest. The laboratory tests, conducted with certified equipment (Alt-8000), demonstrate that the proposed design meets the accuracy standards set by RTCA's DO-155. In addition, this work highlights some challenges and design consideration that comes with this technique.
{"title":"RFSoC-based design and implementation of a Direct RF FMCW radar altimeter","authors":"Victor Bursucianu, A. Amrhar, Jean-Marc Gagné, R. Landry","doi":"10.1109/RadarConf2351548.2023.10149605","DOIUrl":"https://doi.org/10.1109/RadarConf2351548.2023.10149605","url":null,"abstract":"This paper presents the design and performance of a Direct RF Sampling Radar Altimeter based on the RFSoC from Xilinx. This architecture removes the mixing stage by sampling directly from the RF band of interest. The laboratory tests, conducted with certified equipment (Alt-8000), demonstrate that the proposed design meets the accuracy standards set by RTCA's DO-155. In addition, this work highlights some challenges and design consideration that comes with this technique.","PeriodicalId":168311,"journal":{"name":"2023 IEEE Radar Conference (RadarConf23)","volume":"43 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":"124818383","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/RadarConf2351548.2023.10149698
Isabella Lenz, Yu Rong, D. Bliss
In this paper, we delve deeper into recent advancements in radar based biomedical measurements that capture fine movements associated with human heart sounds. We call this measurement the Radarcardiograph (RCG). We analyze the RCG of three subjects to identify distinguishing time and frequency components of the signal. We introduce a parametric signal model as a function of the identified characteristic features. From there, we simultaneously collect and time synchronize the RCG with conventional contact based cardiac interval measurements. We then compare these signals using the Short Time Fourier Transform (STFT), Continuous Wavelet Transform (CWT) and Cochleogram (CLG) for time-frequency analysis. We comment on the similarities and difference of the signals, using the model as reference. Our results improve current understanding of radar based heart sound measurements and provide further validation that radar can be used for non-contact technology heart sound monitoring. We identify limitations in radar based heart sounds measurements. Namely, limited signal quality in the wireless channel, reduced recovered frequency range and weak high frequency components. However, such problem can be addressed via advanced denoising algorithms and system level optimization.
{"title":"Radarcardiograph Signal Modeling and Time-Frequency Analysis","authors":"Isabella Lenz, Yu Rong, D. Bliss","doi":"10.1109/RadarConf2351548.2023.10149698","DOIUrl":"https://doi.org/10.1109/RadarConf2351548.2023.10149698","url":null,"abstract":"In this paper, we delve deeper into recent advancements in radar based biomedical measurements that capture fine movements associated with human heart sounds. We call this measurement the Radarcardiograph (RCG). We analyze the RCG of three subjects to identify distinguishing time and frequency components of the signal. We introduce a parametric signal model as a function of the identified characteristic features. From there, we simultaneously collect and time synchronize the RCG with conventional contact based cardiac interval measurements. We then compare these signals using the Short Time Fourier Transform (STFT), Continuous Wavelet Transform (CWT) and Cochleogram (CLG) for time-frequency analysis. We comment on the similarities and difference of the signals, using the model as reference. Our results improve current understanding of radar based heart sound measurements and provide further validation that radar can be used for non-contact technology heart sound monitoring. We identify limitations in radar based heart sounds measurements. Namely, limited signal quality in the wireless channel, reduced recovered frequency range and weak high frequency components. However, such problem can be addressed via advanced denoising algorithms and system level optimization.","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":"125095022","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/RadarConf2351548.2023.10149556
Deqing Mao, Xingyu Tuo, Jianan Yan, Yulin Huang, Yongchao Zhang, Haiguang Yang, Jianyu Yang
Hybrid regularization methods can be applied in airborne real aperture array radar (RAAR) to improve its angular resolution by combining the advantages of different regularization norms. However, the scale information of the extended targets cannot be accurately obtained because its reconstructed performance is related to the selected regularization parameters. In this paper, to accurately observe the scale information of extended targets, an adaptive hybrid regularization (AHR) method is proposed by a data-adaptive reweighted strategy. First, the generalized sparse (GS) regularization norm and the generalized total variation (GTV) regularization norm are combined to enhance the angular resolution and scale information of extended targets simultaneously. Second, a data-adaptive reweighted strategy is proposed to reduce the number of selected regularization parameters. Finally, simulations are carried out to verify the reconstructed performance of the proposed method. Based on the proposed AHR method, the scale information of the extended targets can be accurately obtained by adaptively selecting proper regularization parameters.
{"title":"Extended Target Reconstruction of Airborne Real Aperture Array Radar by Adaptive Hybrid Regularization","authors":"Deqing Mao, Xingyu Tuo, Jianan Yan, Yulin Huang, Yongchao Zhang, Haiguang Yang, Jianyu Yang","doi":"10.1109/RadarConf2351548.2023.10149556","DOIUrl":"https://doi.org/10.1109/RadarConf2351548.2023.10149556","url":null,"abstract":"Hybrid regularization methods can be applied in airborne real aperture array radar (RAAR) to improve its angular resolution by combining the advantages of different regularization norms. However, the scale information of the extended targets cannot be accurately obtained because its reconstructed performance is related to the selected regularization parameters. In this paper, to accurately observe the scale information of extended targets, an adaptive hybrid regularization (AHR) method is proposed by a data-adaptive reweighted strategy. First, the generalized sparse (GS) regularization norm and the generalized total variation (GTV) regularization norm are combined to enhance the angular resolution and scale information of extended targets simultaneously. Second, a data-adaptive reweighted strategy is proposed to reduce the number of selected regularization parameters. Finally, simulations are carried out to verify the reconstructed performance of the proposed method. Based on the proposed AHR method, the scale information of the extended targets can be accurately obtained by adaptively selecting proper regularization parameters.","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":"123629295","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/RadarConf2351548.2023.10149609
Muhammet Emin Yanik, Sandeep Rao
In this paper, we propose a robust multiple target classification algorithm for real-world complex cluttered environments that can be mapped into low-cost millimeter-wave (mmWave) sensors considering limited memory and processing power budget. A novel approach is developed to create both μ-Doppler and μ-range spectrogram of multiple objects concurrently using an extended Kalman filter (EKF) based tracking layer integration. One-dimensional (1D) time sequence features are extracted from both spectrograms per target object, and a 1D convolutional neural network (CNN) based classifier is built to classify multiple target objects (human or non-human) in the same scene accurately.
{"title":"Radar-Based Multiple Target Classification in Complex Environments Using 1D-CNN Models","authors":"Muhammet Emin Yanik, Sandeep Rao","doi":"10.1109/RadarConf2351548.2023.10149609","DOIUrl":"https://doi.org/10.1109/RadarConf2351548.2023.10149609","url":null,"abstract":"In this paper, we propose a robust multiple target classification algorithm for real-world complex cluttered environments that can be mapped into low-cost millimeter-wave (mmWave) sensors considering limited memory and processing power budget. A novel approach is developed to create both μ-Doppler and μ-range spectrogram of multiple objects concurrently using an extended Kalman filter (EKF) based tracking layer integration. One-dimensional (1D) time sequence features are extracted from both spectrograms per target object, and a 1D convolutional neural network (CNN) based classifier is built to classify multiple target objects (human or non-human) in the same scene accurately.","PeriodicalId":168311,"journal":{"name":"2023 IEEE Radar Conference (RadarConf23)","volume":"22 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":"121962000","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/RadarConf2351548.2023.10149602
Jinyang He, Wanpeng Huang, Ziyang Cheng, Huiyong Li, Zishu Hea
The OFDM sequences with low correlation sidelobe level (CSLL) is desired in many 5G wireless systems. The OFDM sequences and mismatch filter are jointly designed by maximizing the weighted merit factor (WMF) of the cross-correlation between the OFDM sequences and mismatch filter under the constraints of spectra, where MF refers to the ratio of the central lobe energy to the sum of all other lobes. To solve the nonconvex problem, we devise an efficient alternating optimization (AltOpt) algorithm. Numerical simulations are provided to demonstrate the effectiveness of the proposed algorithms.
{"title":"Joint Design of OFDM Sequences and Mismatch Filter under Spectral Constraints","authors":"Jinyang He, Wanpeng Huang, Ziyang Cheng, Huiyong Li, Zishu Hea","doi":"10.1109/RadarConf2351548.2023.10149602","DOIUrl":"https://doi.org/10.1109/RadarConf2351548.2023.10149602","url":null,"abstract":"The OFDM sequences with low correlation sidelobe level (CSLL) is desired in many 5G wireless systems. The OFDM sequences and mismatch filter are jointly designed by maximizing the weighted merit factor (WMF) of the cross-correlation between the OFDM sequences and mismatch filter under the constraints of spectra, where MF refers to the ratio of the central lobe energy to the sum of all other lobes. To solve the nonconvex problem, we devise an efficient alternating optimization (AltOpt) algorithm. Numerical simulations are provided to demonstrate the effectiveness of the proposed algorithms.","PeriodicalId":168311,"journal":{"name":"2023 IEEE Radar Conference (RadarConf23)","volume":"27 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":"122591493","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/RadarConf2351548.2023.10149577
Mandovi Mukherjee, N. M. Rahman, Coleman DeLude, J. Driscoll, Uday Kamal, J. Woo, Jamin Seo, Sudarshan Sharma, Xiangyu Mao, Payman Behnam, Sharjeel Khan, D. Kim, Jianming Tong, Prachi Sinha, S. Pande, T. Krishna, J. Romberg, Madhavan Swaminathan, S. Mukhopadhyay
A high performance architecture for emulating realtime radio frequency systems is presented. The architecture is developed based on a novel compute model and uses nearmemory techniques coupled with highly distributed autonomous control to simultaneously optimize throughput and minimize latency. A cycle level C++ based simulator is used to validate the proposed architecture with simulation of complex RF scenarios.
{"title":"A High Performance Computing Architecture for Real-Time Digital Emulation of RF Interactions","authors":"Mandovi Mukherjee, N. M. Rahman, Coleman DeLude, J. Driscoll, Uday Kamal, J. Woo, Jamin Seo, Sudarshan Sharma, Xiangyu Mao, Payman Behnam, Sharjeel Khan, D. Kim, Jianming Tong, Prachi Sinha, S. Pande, T. Krishna, J. Romberg, Madhavan Swaminathan, S. Mukhopadhyay","doi":"10.1109/RadarConf2351548.2023.10149577","DOIUrl":"https://doi.org/10.1109/RadarConf2351548.2023.10149577","url":null,"abstract":"A high performance architecture for emulating realtime radio frequency systems is presented. The architecture is developed based on a novel compute model and uses nearmemory techniques coupled with highly distributed autonomous control to simultaneously optimize throughput and minimize latency. A cycle level C++ based simulator is used to validate the proposed architecture with simulation of complex RF scenarios.","PeriodicalId":168311,"journal":{"name":"2023 IEEE Radar Conference (RadarConf23)","volume":"74 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":"122774361","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/RadarConf2351548.2023.10149695
Ian Weiner, Houssam Abouzahra, Mitchell LeRoy
In a previous paper, we introduced a flexible methodology for the design of dual-use waveform alphabets which are suitable for simultaneous use in radar and wireless communications. Here we extend this work to accommodate waveforms of constant modulus, and explain how pulse compression constraints may be leveraged to significantly mitigate the significant issue of range-sidelobe modulation. We report results of a field test which validated performance expectations.
{"title":"High-Throughput Communications Using Constant-Modulus Waveforms With Mitigation of Range-Sidelobe Modulation","authors":"Ian Weiner, Houssam Abouzahra, Mitchell LeRoy","doi":"10.1109/RadarConf2351548.2023.10149695","DOIUrl":"https://doi.org/10.1109/RadarConf2351548.2023.10149695","url":null,"abstract":"In a previous paper, we introduced a flexible methodology for the design of dual-use waveform alphabets which are suitable for simultaneous use in radar and wireless communications. Here we extend this work to accommodate waveforms of constant modulus, and explain how pulse compression constraints may be leveraged to significantly mitigate the significant issue of range-sidelobe modulation. We report results of a field test which validated performance expectations.","PeriodicalId":168311,"journal":{"name":"2023 IEEE Radar Conference (RadarConf23)","volume":"109 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":"123177286","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/RadarConf2351548.2023.10149607
M. Malanowski, K. Jędrzejewski, K. Kulpa
The paper presents the concept and its empirical validation of the use of passive bistatic radar based on the LOFAR radio telescope and commercial digital radio DAB+ illuminator for orbit parameter refinement. The orbit parameter update is based on minimizing the errors of bistatic range and velocity measurements in several points in the orbit, visible by the LOFAR receiver.
{"title":"Satellite Orbit Refinement Based on Passive Bistatic Radar Measurements","authors":"M. Malanowski, K. Jędrzejewski, K. Kulpa","doi":"10.1109/RadarConf2351548.2023.10149607","DOIUrl":"https://doi.org/10.1109/RadarConf2351548.2023.10149607","url":null,"abstract":"The paper presents the concept and its empirical validation of the use of passive bistatic radar based on the LOFAR radio telescope and commercial digital radio DAB+ illuminator for orbit parameter refinement. The orbit parameter update is based on minimizing the errors of bistatic range and velocity measurements in several points in the orbit, visible by the LOFAR receiver.","PeriodicalId":168311,"journal":{"name":"2023 IEEE Radar Conference (RadarConf23)","volume":"74 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":"128372867","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/RadarConf2351548.2023.10149620
Murat Temiz, N. Peters, C. Horne, M. Ritchie, C. Masouros
This study experimentally demonstrates a radar-centric integrated sensing and communication (ISAC) system that exploits the radar transmission parameters as modulation indexes to communicate with the user devices while performing short-range radar sensing. The center frequency, bandwidth, and polarization of the transmitted radar chirps are used as modulation indexes. The simulation results have been verified by real-time over-the-air experimental measurements that have also revealed the trade-off between the radar sensing performance and communication data rate, depending on the radar waveform parameters selected in the ISAC system. The proposed dual-function radar and communication system was shown to reach up to 10 Megabits/s throughput depending on the bandwidth and centre frequency separations and chirp duration.
{"title":"Radar-Centric ISAC Through Index Modulation: Over-the-air Experimentation and Trade-offs","authors":"Murat Temiz, N. Peters, C. Horne, M. Ritchie, C. Masouros","doi":"10.1109/RadarConf2351548.2023.10149620","DOIUrl":"https://doi.org/10.1109/RadarConf2351548.2023.10149620","url":null,"abstract":"This study experimentally demonstrates a radar-centric integrated sensing and communication (ISAC) system that exploits the radar transmission parameters as modulation indexes to communicate with the user devices while performing short-range radar sensing. The center frequency, bandwidth, and polarization of the transmitted radar chirps are used as modulation indexes. The simulation results have been verified by real-time over-the-air experimental measurements that have also revealed the trade-off between the radar sensing performance and communication data rate, depending on the radar waveform parameters selected in the ISAC system. The proposed dual-function radar and communication system was shown to reach up to 10 Megabits/s throughput depending on the bandwidth and centre frequency separations and chirp duration.","PeriodicalId":168311,"journal":{"name":"2023 IEEE Radar Conference (RadarConf23)","volume":"83 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":"129984489","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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