系统工程与电子技术
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李宁，祝瑞辉，张勇刚
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LI Ning, ZHU Rui-hui, ZHANG Yong-gang
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摘要：
在目标跟踪中，噪声的统计特性未知可能会引起滤波精度下降甚至发散，针对该问题，提出了一种新的自适应平方根容积卡尔曼滤波算法。所提方法在常规Sage-Husa算法的基础上采用容积规则，推导出了一种适用于非线性系统的自适应噪声统计估计器。仿真结果显示，相对于标准的平方根容积卡尔曼，所提方法在噪声统计特性未知或时变的情况下滤波精度有显著提高。
Abstract:
To solve the problems of low precision and divergence of filter caused by unknown system noise statistics in target tracking, a new adaptive square cubature Kalman filter (CKF) algorithm is proposed. A noise statistics estimator designed for nonlinear systems is derived by applying the cubature rule based on the Sage-Husa algorithm. Simulation results show that as compared with the standard square CKF algorithm, the proposed algorithm provides higher accuracy when the system noise statistics is unknown or time-varying.
李宁，祝瑞辉，张勇刚. 基于Sage-Husa算法的自适应平方根CKF目标跟踪方法[J]. 系统工程与电子技术, doi: 10.3969/j.issn.1001-506X.2014.10.02.
LI Ning, ZHU Rui-hui, ZHANG Yong-gang. Adaptive square CKF method for target tracking based on Sage-Husa algorithm[J]. Systems Engineering and Electronics, doi: 10.3969/j.issn.1001-506X.2014.10.02.
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链接本文: https://www.sys-ele.com/CN/10.3969/j.issn.1001-506X.2014.10.02
https://www.sys-ele.com/CN/Y2014/V36/I10/1899
