A partitioned optimal filtering algorithm for multi-channel systems with multiplicative noise among observation channels is proposed.
针对多通道观测环境下带乘性噪声系统的最优滤波问题，提出了1种状态最优滤波的分部算法。
A new fading filtering algorithm is developed based on the property of Kalman filter that the sequence of residuals is uncorrelated when the optimal gain is used.
本文依据卡尔曼滤波器在使用最佳增益时，其余差序列互不相关的性质，开发了一种新的渐消滤波算法。
A unified algorithm to the optimal filtering, smoothing and prediction is presented, and the asymptotic stability for the initial value of the algorithm is proved.
提出一种统一处理系统最优滤波、预报和平滑估计的新方法，证明了新算法的渐近稳定性。
Based on multiscale optimal filtering fusion, an optimal state fixed-interval smoothing algorithm is developed for systems with multiplicative noise.
针对多尺度带乘性噪声系统，在多尺度最优滤波融合的基础上，进行状态最优固定域平滑算法的研究。
Based on multiscale optimal filtering fusion, an optimal state fixed-interval smoothing algorithm is developed for systems with multiplicative noise.
针对多尺度带乘性噪声系统，在多尺度最优滤波融合的基础上，进行状态最优固定域平滑算法的研究。
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