Stochastic stability of a modified unscented Kalman filter with stochastic nonlinearities and multiple fading measurements
Stochastic stability of a modified unscented Kalman filter with stochastic nonlinearities and multiple fading measurements
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DOI：
10.1016/j.jfranklin.2016.10.028
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
Li Li-Li;Dongdong Yu;Yuanqing Xia;Hongjiu Yang
中科院分区：
文献类型：
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作者：
Li Li-Li;Dongdong Yu;Yuanqing Xia;Hongjiu Yang
针对一类具有随机非线性和多个衰落观测的非线性系统，研究了一种改进的无迹卡尔曼滤波器的随机稳定性。注意，随机非线性由表示乘法随机干扰的统计平均值表示。衰落测量的衰落概率对于每个传感器是单独的。当衰落概率均值存在下界时，证明了改进的无迹卡尔曼滤波器的收敛性。给出了修正无迹卡尔曼滤波器随机稳定的充分条件。数值算例验证了滤波算法的有效性。
Stochastic stability of a modified unscented Kalman filter is investigated for a class of nonlinear systems with stochastic nonlinearities and multiple fading measurements. Note that the stochastic nonlinearities are represented by statistical means which indicate multiplicative stochastic disturbances. The fading probability of fading measurements is individual for each sensor. Convergence on the modified unscented Kalman filter is established if there exists a lower bound for the mean of fading probability. Sufficient conditions are obtained to ensure stochastic stability of the modified unscented Kalman filter. The effectiveness of the filtering algorithm is verified by an illustrative numerical example.