文章基本信息

标题：An Improved Location Algorithm by Extend Square-root Cubature Kalman Filter
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作者：Sheng, Rui Guo ; Zhang, Yang ; Miao, Jun 等
期刊名称：Journal of Computers
印刷版ISSN：1796-203X
出版年度：2013
卷号：8
期号：2
页码：471-477
DOI：10.4304/jcp.8.2.471-477
语种：English
出版社：Academy Publisher
摘要：In this paper, the new nonlinear filter method Cubature Kalman Filter (CKF) is improved to solve the passive location problem. Firstly, the Empirical Mode Decomposition (EMD) algorithm is used to estimate the new measurement noise covariance in the filter process; And then the new covariance of the noise is brought into the circle; Meanwhile, the location process is improved by the way of square root to keep its stability and positivity,and the results of track with moving angle-measured sensors’ measurements by Extend SCKF are compared with the results by Unscented Kalman Filter (UKF) in the paper; By the tracking results to the velocity of the target, Extend SCKF algorithm can not only track the target with unknown measurement noise but also improve the passive position precision remarkably with the same complexity of UKF algorithm.
关键词：Cubature Kalman Filter;Sensor;Empirical Mode Decomposition;Square-Root Cubature Kalman Filter;Unscented Kalman Filter