Bayesian unscented Kalman filter for state estimation of nonlinear and non-Gaussian systems
Bayesian unscented Kalman filter for state estimation of nonlinear and non-Gaussian systems
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DOI：
10.1109/eusipco.2016.7760287
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发表时间：
2016-08
期刊：
影响因子：
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通讯作者：
Zhong Liu;S. Chan;Ho-Chun Wu;Jiafei Wu
中科院分区：
文献类型：
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作者：
Zhong Liu;S. Chan;Ho-Chun Wu;Jiafei Wu
本文提出了一种简化高斯混合的贝叶斯无迹卡尔曼滤波器（BUKF-SGM），用于非线性和非高斯系统的动态状态空间估计。在BUKF-SGM中，状态和噪声密度近似为有限高斯混合，其中每个分量的均值和协方差使用UKF递归估计。为了避免混合成分的指数增长，采用高斯混合简化算法减少混合成分的数量，从而降低了复杂度。实验结果表明，与基于粒子滤波的算法相比，该算法具有更好的性能。这为非线性状态估计问题提供了一种有吸引力的选择。
This paper proposes a Bayesian unscented Kalman filter with simplified Gaussian mixtures (BUKF-SGM) for dynamic state space estimation of nonlinear and non-Gaussian systems. In the BUKF-SGM, the state and noise densities are approximated as finite Gaussian mixtures, in which the mean and covariance for each component are recursively estimated using the UKF. To avoid the exponential growth of mixture components, a Gaussian mixture simplification algorithm is employed to reduce the number of mixture components, which leads to lower complexity in comparing with conventional resampling and clustering techniques. Experimental results show that the proposed BUKF-SGM can achieve better performance compared with the particle filter (PF)-based algorithms. This provides an attractive alternative for nonlinear state estimation problem.