{"title":"A Novel Clutter Covariance Matrix Reconstruction Method for Airborne STAP","authors":"Mingxin Liu, L. Zou, Xue-gang Wang","doi":"10.1109/ICICSP50920.2020.9232020","DOIUrl":null,"url":null,"abstract":"The clutter plus noise covariance matrix (CNCM) usually estimated by the training snapshots is the key to obtain the weight vector in space-time adaptive processing (STAP). However, the CNCM is difficult to estimate accurately in small samples, which affects the target estimation seriously. To solve this problem, a novel CNCM reconstruction method is developed. The proposed method reconstructs the CNCM with Toeplitz structure and then derives closed-form expression for the estimated CNCM. Finally, the weight vector is built, which is convenient to detect and analyze the target signals. The effectiveness of the proposed approach is shown in simulated results.","PeriodicalId":117760,"journal":{"name":"2020 IEEE 3rd International Conference on Information Communication and Signal Processing (ICICSP)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE 3rd International Conference on Information Communication and Signal Processing (ICICSP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICICSP50920.2020.9232020","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The clutter plus noise covariance matrix (CNCM) usually estimated by the training snapshots is the key to obtain the weight vector in space-time adaptive processing (STAP). However, the CNCM is difficult to estimate accurately in small samples, which affects the target estimation seriously. To solve this problem, a novel CNCM reconstruction method is developed. The proposed method reconstructs the CNCM with Toeplitz structure and then derives closed-form expression for the estimated CNCM. Finally, the weight vector is built, which is convenient to detect and analyze the target signals. The effectiveness of the proposed approach is shown in simulated results.
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