The ARMA innovation models play an important role in the optimal filtering.
ARMA 新息模型在最优滤波理论中起重要作用。
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。
It can handle the optimal filtering, smoothing and prediction problems of stationary or non -stationary ARMA signals in the unified framework.
它可统一处理平稳或非平稳arma信号的最优滤波、平滑和预报问题。
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。
The optimal filtering fusion algorithms for systems with multiplicative noises under multi-sensor observation are mainly researched in this dissertation.
本文主要针对带乘性噪声系统的多传感器最优滤波融合估计算法作进一步研究。
This paper proposes a method for recursive optimal filtering of one-dimension stationary ran-dom processes by use of Kalman filtering of constant system.
本文提出了利用定常卡尔曼滤波来实现任意一维平稳随机过程递推最佳滤波的方法。
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。
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。
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