{"title":"小样本情况下的参数到达方向估计","authors":"Manlin Xiao, Xin Qi, P. Wei","doi":"10.1109/IASP.2010.5476063","DOIUrl":null,"url":null,"abstract":"The problem of direction of arrival estimation in array processing is considered in this paper. The focus is on how to estimate the source parameters accurately in the absence of a large number of snapshots. A parametric iterative adaptive algorithm based on amplitude and phase estimation with low computational complexities is proposed in this paper. Eigen-decomposition and the subspace projection are avoided in the proposed algorithm, such that the negative influence of subspace swap phenomena is weakened. This estimator is suitable for DOA estimation in the finite sample-size scenario, especially for resolving closely spaced sources. Numerical evaluations indicate that the estimator has a better performance than the MUSIC algorithm in the small sample-size scenario, where the observation dimension and the sample size are comparable in magnitude.","PeriodicalId":223866,"journal":{"name":"2010 International Conference on Image Analysis and Signal Processing","volume":"47 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Parametric direction of arrival estimation in the small sample-size case\",\"authors\":\"Manlin Xiao, Xin Qi, P. Wei\",\"doi\":\"10.1109/IASP.2010.5476063\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The problem of direction of arrival estimation in array processing is considered in this paper. The focus is on how to estimate the source parameters accurately in the absence of a large number of snapshots. A parametric iterative adaptive algorithm based on amplitude and phase estimation with low computational complexities is proposed in this paper. Eigen-decomposition and the subspace projection are avoided in the proposed algorithm, such that the negative influence of subspace swap phenomena is weakened. This estimator is suitable for DOA estimation in the finite sample-size scenario, especially for resolving closely spaced sources. Numerical evaluations indicate that the estimator has a better performance than the MUSIC algorithm in the small sample-size scenario, where the observation dimension and the sample size are comparable in magnitude.\",\"PeriodicalId\":223866,\"journal\":{\"name\":\"2010 International Conference on Image Analysis and Signal Processing\",\"volume\":\"47 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-04-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 International Conference on Image Analysis and Signal Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IASP.2010.5476063\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 International Conference on Image Analysis and Signal Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IASP.2010.5476063","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Parametric direction of arrival estimation in the small sample-size case
The problem of direction of arrival estimation in array processing is considered in this paper. The focus is on how to estimate the source parameters accurately in the absence of a large number of snapshots. A parametric iterative adaptive algorithm based on amplitude and phase estimation with low computational complexities is proposed in this paper. Eigen-decomposition and the subspace projection are avoided in the proposed algorithm, such that the negative influence of subspace swap phenomena is weakened. This estimator is suitable for DOA estimation in the finite sample-size scenario, especially for resolving closely spaced sources. Numerical evaluations indicate that the estimator has a better performance than the MUSIC algorithm in the small sample-size scenario, where the observation dimension and the sample size are comparable in magnitude.