Convergence of Kalman filter with quantized innovations
Convergence of Kalman filter with quantized innovations
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DOI：
10.1109/icarcv.2010.5707875
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发表时间：
2010-12
期刊：
影响因子：
--
通讯作者：
Jian Xu;Jian-xun Li;Jiayun Wu
中科院分区：
文献类型：
--
作者：
Jian Xu;Jian-xun Li;Jiayun Wu
给出了基于量化测量新息的凯曼滤波估计误差协方差的收敛分析。将量化误差视为观测系统中的随机扰动，给出了一种等效的状态观测系统。因此，原始系统的定量凯曼滤波等价于等价状态观测系统的类卡尔曼滤波。在该性能分析框架中，对估计误差的真实协方差矩阵进行了严格的分析，而不需要对预测分布进行高斯假设。得到了QIKF稳定的充要条件。然后，讨论了标准凯曼滤波与原系统的QIKF之间的关系。最后，通过数值模拟验证了这些结果的有效性。
This work provides a convergence analysis for the estimate error covariance of Kaiman filtering based on quantized measurement innovations (QIKF). By taking the quantization errors as random perturbations in observation system, an equivalent state-observation system is given. Accordingly, the quantitative Kaiman filter for the original system is equivalent to a Kalman-like filtering for the equivalent state-observation system. In this performance analysis framework, the true covariance matrix of estimating error is strictly analyzed without Gaussian assumption on predicted distribution. A necessary and sufficient condition for the stability of the QIKF is obtained. Then, the relationship between the standard Kaiman filtering and the QIKF for the original system is discussed. Finally, the validity of these results are demonstrated by numerical simulations.