Adaptive Gaussian sum squared-root cubature Kalman filter with split-merge scheme for state estimation  

作　　者：Liu Yu Dong Kai Wang Haipeng Liu Jun He You Pan Lina 

机构地区：[1]Institute of Information Fusion, Naval Aeronautical and Astronautical University

出　　处：《Chinese Journal of Aeronautics》2014年第5期1242-1250,共9页中国航空学报（英文版）

基　　金：supported by the National Natural Science Foundation of China(No. 61032001);Shandong Provincial Natural Science Foundation of China (No. ZR2012FQ004)

摘　　要：The paper deals with state estimation problem of nonlinear non-Gaussian discrete dynamic systems for improvement of accuracy and consistency. An efficient new algorithm called the adaptive Gaussian-sum square-root cubature Kalman filter（AGSSCKF） with a split-merge scheme is proposed. It is developed based on the squared-root extension of newly introduced cubature Kalman filter（SCKF） and is built within a Gaussian-sum framework. Based on the condition that the probability density functions of process noises and initial state are denoted by a Gaussian sum using optimization method, a bank of SCKF are used as the sub-filters to estimate state of system with the corresponding weights respectively, which is adaptively updated. The new algorithm consists of an adaptive splitting and merging procedure according to a proposed split-decision model based on the nonlinearity degree of measurement. The results of two simulation scenarios（one-dimensional state estimation and bearings-only tracking） show that the proposed filter demonstrates comparable performance to the particle filter with significantly reduced computational cost.The paper deals with state estimation problem of nonlinear non-Gaussian discrete dynamic systems for improvement of accuracy and consistency. An efficient new algorithm called the adaptive Gaussian-sum square-root cubature Kalman filter（AGSSCKF） with a split-merge scheme is proposed. It is developed based on the squared-root extension of newly introduced cubature Kalman filter（SCKF） and is built within a Gaussian-sum framework. Based on the condition that the probability density functions of process noises and initial state are denoted by a Gaussian sum using optimization method, a bank of SCKF are used as the sub-filters to estimate state of system with the corresponding weights respectively, which is adaptively updated. The new algorithm consists of an adaptive splitting and merging procedure according to a proposed split-decision model based on the nonlinearity degree of measurement. The results of two simulation scenarios（one-dimensional state estimation and bearings-only tracking） show that the proposed filter demonstrates comparable performance to the particle filter with significantly reduced computational cost.

关 键 词：Adaptive split-merge scheme Gaussian sum filter Nonlinear non-Gaussian State estimation Squared-root cubature Kalman filter 

分 类 号：TN713[电子电信—电路与系统]