Robust structured total least squares algorithm for passive location

A new approach called the robust structured total least squares （RSTLS） algorithm is described for solving location inac- curacy caused by outliers in the single-observer passive location. It is built within the weighted structured total least squares （WSTLS） framework and improved based on the robu...

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Bibliographic Details
Published in系统科学与复杂性：英文版 no. 5; pp. 946 - 953
Main Author Hao Wu Shuxin Chen Yihang Zhang Hengyang Zhang Juan Ni
Format Journal Article
LanguageEnglish
Published 2015
Subjects
TLS问题
单站无源定位
异常值
总体最小二乘法
最小二乘算法
权重函数
结构总体
鲁棒
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Summary:A new approach called the robust structured total least squares （RSTLS） algorithm is described for solving location inac- curacy caused by outliers in the single-observer passive location. It is built within the weighted structured total least squares （WSTLS） framework and improved based on the robust estimation theory. Moreover, the improved Danish weight function is proposed ac- cording to the robust extremal function of the WSTLS, so that the new algorithm can detect outliers based on residuals and reduce the weights of outliers automatically. Finally, the inverse iteration method is discussed to deal with the RSTLS problem. Simulations show that when outliers appear, the result of the proposed algo- rithm is still accurate and robust, whereas that of the conventional algorithms is distorted seriously.
Bibliography:Hao Wu, Shuxin Chen, Yihang Zhang, Hengyang Zhang, and Juan Ni（ 1. Information and Navigation College, Air Force Engineering University, Xi＇an 710077, China; 2. Unit 94303 of PLA, Weifang 261051, China）
11-4543/O1
passive location, structured total least squares, robustestimation, equivalent weight function.
A new approach called the robust structured total least squares （RSTLS） algorithm is described for solving location inac- curacy caused by outliers in the single-observer passive location. It is built within the weighted structured total least squares （WSTLS） framework and improved based on the robust estimation theory. Moreover, the improved Danish weight function is proposed ac- cording to the robust extremal function of the WSTLS, so that the new algorithm can detect outliers based on residuals and reduce the weights of outliers automatically. Finally, the inverse iteration method is discussed to deal with the RSTLS problem. Simulations show that when outliers appear, the result of the proposed algo- rithm is still accurate and robust, whereas that of the conventional algorithms is distorted seriously.
ISSN:1009-6124
1559-7067