Pub Date : 2019-09-30DOI: 10.1017/9781108552653.003
{"title":"Clutter Rejection and Adaptive Filtering in Compressed Sensing Radar","authors":"","doi":"10.1017/9781108552653.003","DOIUrl":"https://doi.org/10.1017/9781108552653.003","url":null,"abstract":"","PeriodicalId":251232,"journal":{"name":"Compressed Sensing in Radar Signal Processing","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117212573","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 : 2019-09-30DOI: 10.1017/9781108552653.013
{"title":"Index","authors":"","doi":"10.1017/9781108552653.013","DOIUrl":"https://doi.org/10.1017/9781108552653.013","url":null,"abstract":"","PeriodicalId":251232,"journal":{"name":"Compressed Sensing in Radar Signal Processing","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127723475","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 : 2019-09-30DOI: 10.1017/9781108552653.011
{"title":"Cooperative Spectrum Sharing between Sparse Sensing-Based Radar and Communication Systems","authors":"","doi":"10.1017/9781108552653.011","DOIUrl":"https://doi.org/10.1017/9781108552653.011","url":null,"abstract":"","PeriodicalId":251232,"journal":{"name":"Compressed Sensing in Radar Signal Processing","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133193056","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 : 2019-09-30DOI: 10.1017/9781108552653.007
{"title":"Fast and Robust Sparsity-Based STAP Methods for Nonhomogeneous Clutter","authors":"","doi":"10.1017/9781108552653.007","DOIUrl":"https://doi.org/10.1017/9781108552653.007","url":null,"abstract":"","PeriodicalId":251232,"journal":{"name":"Compressed Sensing in Radar Signal Processing","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121866720","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 : 2019-09-30DOI: 10.1017/9781108552653.004
{"title":"RFI Mitigation Based on Compressive Sensing Methods for UWB Radar Imaging","authors":"","doi":"10.1017/9781108552653.004","DOIUrl":"https://doi.org/10.1017/9781108552653.004","url":null,"abstract":"","PeriodicalId":251232,"journal":{"name":"Compressed Sensing in Radar Signal Processing","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116755893","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 : 2019-09-30DOI: 10.1017/9781108552653.012
{"title":"Compressed Sensing Methods for Radar Imaging in the Presence of Phase Errors and Moving Objects","authors":"","doi":"10.1017/9781108552653.012","DOIUrl":"https://doi.org/10.1017/9781108552653.012","url":null,"abstract":"","PeriodicalId":251232,"journal":{"name":"Compressed Sensing in Radar Signal Processing","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117090142","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 : 2019-09-30DOI: 10.1017/9781108552653.005
{"title":"Compressed CFAR Techniques","authors":"","doi":"10.1017/9781108552653.005","DOIUrl":"https://doi.org/10.1017/9781108552653.005","url":null,"abstract":"","PeriodicalId":251232,"journal":{"name":"Compressed Sensing in Radar Signal Processing","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124773484","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 : 2019-09-30DOI: 10.1017/9781108552653.010
{"title":"Spectrum Sensing for Cognitive Radar via Model Sparsity Exploitation","authors":"","doi":"10.1017/9781108552653.010","DOIUrl":"https://doi.org/10.1017/9781108552653.010","url":null,"abstract":"","PeriodicalId":251232,"journal":{"name":"Compressed Sensing in Radar Signal Processing","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132965478","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 : 1900-01-01DOI: 10.1017/9781108552653.009
Yujie Gu, N. Goodman, Yimin D. Zhang
Traditional adaptive beamformers are very sensitive to model mismatch, especially when the training samples for adaptive beamformer design are contaminated by the desired signal. In this chapter, we reconstruct a signal-free interference-plus-noise covariance matrix for adaptive beamformer design. Exploiting the sparsity of sources, the interference covariance matrix can be reconstructed as a weighted sum of the outer products of the interference steering vectors, and the corresponding parameters can be estimated from a sparsityconstrained covariance matrix fitting problem. In contrast to classical compressive sensing and sparse reconstruction techniques, the sparsity-constrained covariance matrix fitting problem can be effectively solved as a modified least squares solution by using the a priori information on the array structure. Extensive simulation results demonstrate that the proposed adaptive beamformer almost always provides the near-optimal output performance regardless of the input signal power.
{"title":"Adaptive Beamforming via Sparsity-Based Reconstruction of Covariance Matrix","authors":"Yujie Gu, N. Goodman, Yimin D. Zhang","doi":"10.1017/9781108552653.009","DOIUrl":"https://doi.org/10.1017/9781108552653.009","url":null,"abstract":"Traditional adaptive beamformers are very sensitive to model mismatch, especially when the training samples for adaptive beamformer design are contaminated by the desired signal. In this chapter, we reconstruct a signal-free interference-plus-noise covariance matrix for adaptive beamformer design. Exploiting the sparsity of sources, the interference covariance matrix can be reconstructed as a weighted sum of the outer products of the interference steering vectors, and the corresponding parameters can be estimated from a sparsityconstrained covariance matrix fitting problem. In contrast to classical compressive sensing and sparse reconstruction techniques, the sparsity-constrained covariance matrix fitting problem can be effectively solved as a modified least squares solution by using the a priori information on the array structure. Extensive simulation results demonstrate that the proposed adaptive beamformer almost always provides the near-optimal output performance regardless of the input signal power.","PeriodicalId":251232,"journal":{"name":"Compressed Sensing in Radar Signal Processing","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134005233","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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