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Adaptive Gaussian sum squared-root cubature Kalman filter with split-merge scheme for state estimation 被引量:5

Adaptive Gaussian sum squared-root cubature Kalman filter with split-merge scheme for state estimation
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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.
出处 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 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)
关键词 Adaptive split-merge scheme Gaussian sum filter Nonlinear non-Gaussian State estimation Squared-root cubature Kalman filter Adaptive split-merge scheme Gaussian sum filter Nonlinear non-Gaussian State estimation Squared-root cubature Kalman filter
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