Cubature information filters with correlated noises and their applications in decentralized fusion
Cubature information filters with correlated noises and their applications in decentralized fusion
复制标题
具有相关噪声的体积信息滤波器及其在分散融合中的应用
DOI：
10.1016/j.sigpro.2013.06.015
复制
发表时间：
2014-01-01
影响因子：
4.4
通讯作者：
Wen, Chenglin
中科院分区：
文献类型：
--
作者：
Ge, Quanbo;Xu, Daxing;Wen, Chenglin
非线性系统的数据融合是近年来状态估计和目标跟踪领域的一个具有挑战性的课题。本文研究分散容积卡尔曼融合。立方卡尔曼滤波（CKF）是一种比传统的非线性滤波（如扩展卡尔曼滤波（EKF）和无迹卡尔曼滤波（UKF））更有效的方法。对于大多数实际情况，过程噪声和测量噪声之间存在相关性（相关性I），测量噪声之间存在相关性（相关性II）。因此，对于复杂相关噪声系统，设计基于CKF的融合算法更具有吸引力。首先，推导了一种具有相关性I的容积卡尔曼滤波器（CKF-CN）。其次，通过引入相关噪声扩展卡尔曼滤波器（EKF-CN）及其信息滤波器EIF-CN，将CKF-CN嵌入EIF-CN框架中，得到相关噪声体积信息滤波器（CIF-CN）。因此，提出了噪声相关平方根容积卡尔曼滤波器（SCKF-CN）和相关信息滤波器SCIF-CN，以提高计算性能。最后，基于所提出的SCIF-CN和矩阵对角化，提出了一种分散的非线性融合算法的多传感器系统的相关性I和相关性II。仿真实例验证了所提出的滤波器和融合算法的有效性。(C)2013年由Elsevier B. V.出版
Data fusion for nonlinear systems is one of the challenging topics in state estimation and target tracking recently. We study decentralized cubature Kalman fusion in this paper. Cubature Kalman filter (CKF) is a more effective method than the conventional nonlinear filters, such as extended Kalman filter (EKF) and unscented Kalman filter (UKF). For most of the practical cases, there are correlative between process and measurement noises (Correlation I) and among measurement noises (Correlation II). So, it is more attractive to design fusion algorithms based on the CKF for the systems with complex correlated noises. Firstly, a cubature Kalman filter with correlation I (CKF-CN) is derived. Secondly, by introducing the EKF with correlated noises (EKF-CN) and its information filter EIF-CN, the CKF-CN is embedded in the EIF-CN framework to get a cubature information filter with correlated noises (CIF-CN). Consequently, a square-root cubature Kalman filter with noise correlation I (SCKF-CN) and the associated information filter SCIF-CN are presented to improve computational performance. Finally, based on the proposed SCIF-CN and matrix diagonalization, a decentralized nonlinear fusion algorithm is proposed for the multisensor system with Correlation I and Correlation II. Simulation examples are demonstrated to validate the proposed filters and fusion algorithms. (C) 2013 Published by Elsevier B.V.