Pub Date : 2023-05-01DOI: 10.1109/RadarConf2351548.2023.10149740
Samia Kazemi, Bariscan Yonel, B. Yazıcı
In this paper, we designed a deep learning (DL) based method for synthetic aperture imaging in the presence of phase errors. Random variations in the transmission medium resulting from unforeseen environmental changes, fluctuations in sensor locations, and multiple scattering effects in the background medium often amount to uncertainties in the assumed data models. Imaging algorithms that rely on back-projected estimates are susceptible to estimation errors under these circumstances. Moreover, under dynamic nature of the medium, collecting high volume of measurements under the same operating conditions may become challenging. Towards this end, our imaging network incorporates DL in three major steps: first, we implement a deep network (DN) for pre-processing the erroneous measurements; second, we implement a DL-based decoding prior by recovering an encoded version of the reflectivity vector associated with the scattering media to reduce sample complexity, which is then mapped to an image estimate by a decoding DN; finally, we consider a fixed step implementation of an iterative algorithm in the form of a recurrent neural network (RNN) by using the unrolling technique that leads to a model-based imaging operator. The parameters of all three DNs are learned simultaneously in a supervised manner. We verified the feasibility of our approach using simulated high fidelity synthetic aperture measurements.
{"title":"Deep Learning based Synthetic Aperture Imaging in the Presence of Phase Errors via Decoding Priors","authors":"Samia Kazemi, Bariscan Yonel, B. Yazıcı","doi":"10.1109/RadarConf2351548.2023.10149740","DOIUrl":"https://doi.org/10.1109/RadarConf2351548.2023.10149740","url":null,"abstract":"In this paper, we designed a deep learning (DL) based method for synthetic aperture imaging in the presence of phase errors. Random variations in the transmission medium resulting from unforeseen environmental changes, fluctuations in sensor locations, and multiple scattering effects in the background medium often amount to uncertainties in the assumed data models. Imaging algorithms that rely on back-projected estimates are susceptible to estimation errors under these circumstances. Moreover, under dynamic nature of the medium, collecting high volume of measurements under the same operating conditions may become challenging. Towards this end, our imaging network incorporates DL in three major steps: first, we implement a deep network (DN) for pre-processing the erroneous measurements; second, we implement a DL-based decoding prior by recovering an encoded version of the reflectivity vector associated with the scattering media to reduce sample complexity, which is then mapped to an image estimate by a decoding DN; finally, we consider a fixed step implementation of an iterative algorithm in the form of a recurrent neural network (RNN) by using the unrolling technique that leads to a model-based imaging operator. The parameters of all three DNs are learned simultaneously in a supervised manner. We verified the feasibility of our approach using simulated high fidelity synthetic aperture measurements.","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":"122820271","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/RadarConf2351548.2023.10149735
Hovannes Kulhandjian, Alexander Davis, Lancelot Leong, Michael Bendot, Michel Kulhandjian
One of the main challenges currently firefighters are facing in search and rescue operations is battling the heavy smoke inside a space that needs to be searched for people and animals. In this work, we develop an integrated system composed of two unique sensing mechanisms that are capable of real-time detection and localization of humans and animals in deep smoke to improve the situational awareness of firefighters on the scene. We make use of data from a micro-Doppler sensor and an infrared camera and train a DCNN algorithm to localize a human in dense smoke in real-time. Experimental results reveal that the proposed system can detect a human in heavy smoke with an averaae of 98 % validation accuracy.
{"title":"AI-based Human Detection and Localization in Heavy Smoke using Radar and IR Camera","authors":"Hovannes Kulhandjian, Alexander Davis, Lancelot Leong, Michael Bendot, Michel Kulhandjian","doi":"10.1109/RadarConf2351548.2023.10149735","DOIUrl":"https://doi.org/10.1109/RadarConf2351548.2023.10149735","url":null,"abstract":"One of the main challenges currently firefighters are facing in search and rescue operations is battling the heavy smoke inside a space that needs to be searched for people and animals. In this work, we develop an integrated system composed of two unique sensing mechanisms that are capable of real-time detection and localization of humans and animals in deep smoke to improve the situational awareness of firefighters on the scene. We make use of data from a micro-Doppler sensor and an infrared camera and train a DCNN algorithm to localize a human in dense smoke in real-time. Experimental results reveal that the proposed system can detect a human in heavy smoke with an averaae of 98 % validation accuracy.","PeriodicalId":168311,"journal":{"name":"2023 IEEE Radar Conference (RadarConf23)","volume":"10 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":"127769840","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/RadarConf2351548.2023.10149772
Fangzhou Wang, A. L. Swindlehurst, Hongbin Li
Dual-function radar-communication (DFRC) design is a promising approach for solving the challenging spectrum congestion problem. This paper considers joint antenna selection and digital beamforming design for a DFRC system that serves multiple multicast communication groups and, meanwhile, performs sensing. The dual-function transmit design is cast as maximizing the minimum target illumination power in multiple target directions by jointly selecting the antennas and designing the beamformers subject to a lower bound on the signal-to-interference-plus-noise ratio (SINR) for the communication users and an upper bound on the clutter power at each clutter scatterer. The resulting optimization formulation is a mixed integer programming problem that is solved with a penalized sequential convex relaxation scheme along with semidefinite relaxation (SDR). Numerical results verify the effectiveness of the proposed DFRC scheme and the associated algorithm.
{"title":"Joint Antenna Selection and Transmit Beamforming for Dual-Function Radar-Communication Systems","authors":"Fangzhou Wang, A. L. Swindlehurst, Hongbin Li","doi":"10.1109/RadarConf2351548.2023.10149772","DOIUrl":"https://doi.org/10.1109/RadarConf2351548.2023.10149772","url":null,"abstract":"Dual-function radar-communication (DFRC) design is a promising approach for solving the challenging spectrum congestion problem. This paper considers joint antenna selection and digital beamforming design for a DFRC system that serves multiple multicast communication groups and, meanwhile, performs sensing. The dual-function transmit design is cast as maximizing the minimum target illumination power in multiple target directions by jointly selecting the antennas and designing the beamformers subject to a lower bound on the signal-to-interference-plus-noise ratio (SINR) for the communication users and an upper bound on the clutter power at each clutter scatterer. The resulting optimization formulation is a mixed integer programming problem that is solved with a penalized sequential convex relaxation scheme along with semidefinite relaxation (SDR). Numerical results verify the effectiveness of the proposed DFRC scheme and the associated algorithm.","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":"128611347","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/RadarConf2351548.2023.10149638
Michael V. Lipski, S. Kompella, R. Narayanan
Collaborative transmit beamforming using coherent distributed arrays is a method by which multiple wireless nodes synchronize their transmissions in order to synthesize a virtual antenna array. Assuming a high degree of inter-node synchronization in frequency, phase, and location, wireless transmitters can synthesize a beam with a power gain of $N^{2}$ towards any arbitrary direction. Using a planar model, we examine the use of global optimization algorithms to add additional beams or nulls to the transmit pattern of a distributed array. Using simulations, we show that adjusting the positions of array nodes can reliably create multiple side beams or nulls in desired azimuth locations. Simulated annealing and pattern search optimization methods are explored for both beam forming and nulling and are compared across the average side beam intensity, node displacement, and algorithm running time.
{"title":"A Pattern Shaping Approach for Distributed Collaborative Beamforming","authors":"Michael V. Lipski, S. Kompella, R. Narayanan","doi":"10.1109/RadarConf2351548.2023.10149638","DOIUrl":"https://doi.org/10.1109/RadarConf2351548.2023.10149638","url":null,"abstract":"Collaborative transmit beamforming using coherent distributed arrays is a method by which multiple wireless nodes synchronize their transmissions in order to synthesize a virtual antenna array. Assuming a high degree of inter-node synchronization in frequency, phase, and location, wireless transmitters can synthesize a beam with a power gain of $N^{2}$ towards any arbitrary direction. Using a planar model, we examine the use of global optimization algorithms to add additional beams or nulls to the transmit pattern of a distributed array. Using simulations, we show that adjusting the positions of array nodes can reliably create multiple side beams or nulls in desired azimuth locations. Simulated annealing and pattern search optimization methods are explored for both beam forming and nulling and are compared across the average side beam intensity, node displacement, and algorithm running time.","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":"115979909","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/RadarConf2351548.2023.10149655
Bingcheng C. Li
Since a frequency modulation signal can be approximated by polynomial chirplet in a local time window, polynomial chirplet transform has been applied to acoustic signal processing, radar Doppler analysis and gravity wave analysis. However, the direct implementation of a polynomial chirplet transform has extremely high computational cost due to its high dimensional polynomial chirplet parameter space. In this paper, we propose a spectrogram time-frequency filtering and ridge graph polynomial fitting approach to estimate polynomial chirplet parameters for the time-frequency analysis. In the proposed method, a low dimensional spectrogram ridge graph fitting is developed to extract high dimensional polynomial chirplet parameters for the computational cost reduction. Furthermore, the spectrogram filtering in the time-frequency space is proposed to improve the reliability of spectrogram ridge extraction, and a ridge interpolation technique is recommended to improve the accuracy of ridge extraction. Test results show that the proposed method has a low computational cost, high reliability and accuracy for extracting polynomial chirplet parameters.
{"title":"Spectrogram Filtering and Ridge Graph Fitting Based Time Frequency Analysis","authors":"Bingcheng C. Li","doi":"10.1109/RadarConf2351548.2023.10149655","DOIUrl":"https://doi.org/10.1109/RadarConf2351548.2023.10149655","url":null,"abstract":"Since a frequency modulation signal can be approximated by polynomial chirplet in a local time window, polynomial chirplet transform has been applied to acoustic signal processing, radar Doppler analysis and gravity wave analysis. However, the direct implementation of a polynomial chirplet transform has extremely high computational cost due to its high dimensional polynomial chirplet parameter space. In this paper, we propose a spectrogram time-frequency filtering and ridge graph polynomial fitting approach to estimate polynomial chirplet parameters for the time-frequency analysis. In the proposed method, a low dimensional spectrogram ridge graph fitting is developed to extract high dimensional polynomial chirplet parameters for the computational cost reduction. Furthermore, the spectrogram filtering in the time-frequency space is proposed to improve the reliability of spectrogram ridge extraction, and a ridge interpolation technique is recommended to improve the accuracy of ridge extraction. Test results show that the proposed method has a low computational cost, high reliability and accuracy for extracting polynomial chirplet parameters.","PeriodicalId":168311,"journal":{"name":"2023 IEEE Radar Conference (RadarConf23)","volume":"19 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":"116015670","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/RadarConf2351548.2023.10149703
Mayeul Jeannin, O. Lang, D. Nugraha, Farhan Bin Khalid, André Roger, M. Huemer
Frequency modulated continuous wave (FMCW) multiple input multiple output (MIMO) radar systems employing Doppler division multiplexing (DDM) require calibrating the phase shifters to maintain optimal performance and minimize spurs all along the radar system's life cycle. Existing methods rely on a dedicated time slot to perform the phase shifters' calibration. Ideally, a method allowing for online estimation of the phase shifter imbalances without interrupting the radar operation would be desirable. In this work, a particular subset of the DDM coding scheme is analyzed, enabling such an online estimation. The proposed coding scheme and associated constellation imbalance estimator are detailed in this work and tested on the road, showing the robustness of the estimator.
{"title":"Particular DDM codes for online phase shifter calibration in automotive MIMO radar","authors":"Mayeul Jeannin, O. Lang, D. Nugraha, Farhan Bin Khalid, André Roger, M. Huemer","doi":"10.1109/RadarConf2351548.2023.10149703","DOIUrl":"https://doi.org/10.1109/RadarConf2351548.2023.10149703","url":null,"abstract":"Frequency modulated continuous wave (FMCW) multiple input multiple output (MIMO) radar systems employing Doppler division multiplexing (DDM) require calibrating the phase shifters to maintain optimal performance and minimize spurs all along the radar system's life cycle. Existing methods rely on a dedicated time slot to perform the phase shifters' calibration. Ideally, a method allowing for online estimation of the phase shifter imbalances without interrupting the radar operation would be desirable. In this work, a particular subset of the DDM coding scheme is analyzed, enabling such an online estimation. The proposed coding scheme and associated constellation imbalance estimator are detailed in this work and tested on the road, showing the robustness of the estimator.","PeriodicalId":168311,"journal":{"name":"2023 IEEE Radar Conference (RadarConf23)","volume":"14 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":"116827016","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/RadarConf2351548.2023.10149587
Gustavo F. Araujo, Renato B. Machado, M. Pettersson
This article proposes a method to simulate Synthetic Aperture Radar (SAR) targets for specific incidence and azimuth angles. Images synthesized by Electromagnetic Computing (EMC) are used to train a Conditional Generative Adversarial Network (cGAN). Two synthetic image chips of the same class and incidence angle, separated by two degrees in azimuth, are used as input to the cGAN. The cGAN predicts the image of the same class and incidence angle whose azimuth angle corresponds to the bisector of the two input chips. An evaluation using the SAMPLE dataset was performed to verify the quality of the image prediction. Running through a total of 100 training epochs, the cGAN converges, reaching the best Mean Squared Error (MSE) after 77 epochs. The results demonstrate that the proposed method is promising for Automatic Target Recognition (ATR) applications.
{"title":"A Tailored cGAN SAR Synthetic Data Augmentation Method for ATR Application","authors":"Gustavo F. Araujo, Renato B. Machado, M. Pettersson","doi":"10.1109/RadarConf2351548.2023.10149587","DOIUrl":"https://doi.org/10.1109/RadarConf2351548.2023.10149587","url":null,"abstract":"This article proposes a method to simulate Synthetic Aperture Radar (SAR) targets for specific incidence and azimuth angles. Images synthesized by Electromagnetic Computing (EMC) are used to train a Conditional Generative Adversarial Network (cGAN). Two synthetic image chips of the same class and incidence angle, separated by two degrees in azimuth, are used as input to the cGAN. The cGAN predicts the image of the same class and incidence angle whose azimuth angle corresponds to the bisector of the two input chips. An evaluation using the SAMPLE dataset was performed to verify the quality of the image prediction. Running through a total of 100 training epochs, the cGAN converges, reaching the best Mean Squared Error (MSE) after 77 epochs. The results demonstrate that the proposed method is promising for Automatic Target Recognition (ATR) applications.","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":"114261794","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/RadarConf2351548.2023.10149798
A. Bekar, M. Antoniou, C. Baker
This paper presents initial results of SAR imaging and change detection for a high-resolution (6×30cm), short-range drone-borne radar system. A novel hybrid approach to change detection is developed using the main SAR system parameters of altitude, look angle, and range to the scene center. These are used to determine the derived change detection sensitivity and to identify and quantify image decorrelation, a basic measure of change detection performance. An overview of the algorithm developed to generate incoherent/coherent change maps is also presented. In order to examine this approach to change detection on a practical basis, a high-resolution 24 GHz drone-borne SAR system is used, for the first time, to demonstrate and quantify performance based on real-world experiments.
{"title":"Change Detection for High-Resolution Drone-Borne SAR at High Frequencies - First Results","authors":"A. Bekar, M. Antoniou, C. Baker","doi":"10.1109/RadarConf2351548.2023.10149798","DOIUrl":"https://doi.org/10.1109/RadarConf2351548.2023.10149798","url":null,"abstract":"This paper presents initial results of SAR imaging and change detection for a high-resolution (6×30cm), short-range drone-borne radar system. A novel hybrid approach to change detection is developed using the main SAR system parameters of altitude, look angle, and range to the scene center. These are used to determine the derived change detection sensitivity and to identify and quantify image decorrelation, a basic measure of change detection performance. An overview of the algorithm developed to generate incoherent/coherent change maps is also presented. In order to examine this approach to change detection on a practical basis, a high-resolution 24 GHz drone-borne SAR system is used, for the first time, to demonstrate and quantify performance based on real-world experiments.","PeriodicalId":168311,"journal":{"name":"2023 IEEE Radar Conference (RadarConf23)","volume":"26 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":"114220688","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/RadarConf2351548.2023.10149683
Chen Ning, J. Tian, Shanling Zheng, Biao Zhang, W. Cui
The existing iterative adaptive filtering algorithms based on the minimum mean square error (MMSE) criterion can effectively suppress range-Doppler sidelobes of targets and unveil targets in multi-target scenarios. However, these iterative adaptive filtering algorithms suffer from deteriorated performance in the presence of targets with range-Doppler-straddling when using linear frequency modulation (LFM) waveforms. To suppress sidelobes when straddling occurs, this paper presents a robust adaptive pulse compression algorithm based on straddling- robust self-calibration iterative adaptive filtering (SR-SCIAF). The received signal model considering range-Doppler-straddling effects are firstly established and then the SR-SCIAF algorithm is introduced based on the MMSE criterion. During the iterative processing, SR-SCIAF can reduce the modelling mismatch by estimating straddling offsets and compensating the corresponding phase mismatch. Simulation results demonstrate that SR- SCIAF provides good robustness against straddling effects and can effectively suppress range-Doppler sidelobes of targets with straddling in multi-target scenarios.
{"title":"Robust Adaptive Pulse Compression Algorithm for Targets with Straddling","authors":"Chen Ning, J. Tian, Shanling Zheng, Biao Zhang, W. Cui","doi":"10.1109/RadarConf2351548.2023.10149683","DOIUrl":"https://doi.org/10.1109/RadarConf2351548.2023.10149683","url":null,"abstract":"The existing iterative adaptive filtering algorithms based on the minimum mean square error (MMSE) criterion can effectively suppress range-Doppler sidelobes of targets and unveil targets in multi-target scenarios. However, these iterative adaptive filtering algorithms suffer from deteriorated performance in the presence of targets with range-Doppler-straddling when using linear frequency modulation (LFM) waveforms. To suppress sidelobes when straddling occurs, this paper presents a robust adaptive pulse compression algorithm based on straddling- robust self-calibration iterative adaptive filtering (SR-SCIAF). The received signal model considering range-Doppler-straddling effects are firstly established and then the SR-SCIAF algorithm is introduced based on the MMSE criterion. During the iterative processing, SR-SCIAF can reduce the modelling mismatch by estimating straddling offsets and compensating the corresponding phase mismatch. Simulation results demonstrate that SR- SCIAF provides good robustness against straddling effects and can effectively suppress range-Doppler sidelobes of targets with straddling in multi-target scenarios.","PeriodicalId":168311,"journal":{"name":"2023 IEEE Radar Conference (RadarConf23)","volume":"38 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":"126900287","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/RadarConf2351548.2023.10149745
Rui Tan, Maosen Liao, Yi Bu, Xianxiang Yu, G. Cui
This paper deals with the suppression of multiple mainlobe blanket jamming for a distributed radar system via an interesting cooperative waveform strategy in frequency domain. Specifically, we consider three sites including one transmitting/receiving site and two transmitting sites with short baseline arrangement. We herein design a narrowband detecting signal with good autocorrelation for the transmitting/receiving site, and desired narrowband deceiving and wideband protecting signals for other two transmitting sites. By doing so, a complex spectrum can be formed when the three signals arrive at the enemy jammers via reasonably controlling the powers and launch timings of the three sites. Thus, the enemy jammers can not identify the real detecting signal resulting in degradation of the jamming efficiency. The numerical simulations are conducted to assess the performance of the proposed method compared with the classic distributed Multiple-Input Multiple-Output (MIMO) mode and Single-Input Single-Output (SISO) mode.
{"title":"Cooperative Waveforms Design for Distributed Radars in Multiple Blanket Jamming","authors":"Rui Tan, Maosen Liao, Yi Bu, Xianxiang Yu, G. Cui","doi":"10.1109/RadarConf2351548.2023.10149745","DOIUrl":"https://doi.org/10.1109/RadarConf2351548.2023.10149745","url":null,"abstract":"This paper deals with the suppression of multiple mainlobe blanket jamming for a distributed radar system via an interesting cooperative waveform strategy in frequency domain. Specifically, we consider three sites including one transmitting/receiving site and two transmitting sites with short baseline arrangement. We herein design a narrowband detecting signal with good autocorrelation for the transmitting/receiving site, and desired narrowband deceiving and wideband protecting signals for other two transmitting sites. By doing so, a complex spectrum can be formed when the three signals arrive at the enemy jammers via reasonably controlling the powers and launch timings of the three sites. Thus, the enemy jammers can not identify the real detecting signal resulting in degradation of the jamming efficiency. The numerical simulations are conducted to assess the performance of the proposed method compared with the classic distributed Multiple-Input Multiple-Output (MIMO) mode and Single-Input Single-Output (SISO) mode.","PeriodicalId":168311,"journal":{"name":"2023 IEEE Radar Conference (RadarConf23)","volume":"80 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":"125969281","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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