自校正Kalman滤波、预报、去卷、平滑新方法

A New Approach to Self-Tuning Kalman Filtering Prediction, Deconvolution and Smoothing

中文关键词  稳态最优滤波  自校正滤波  去卷  平滑  白噪声估值器

英文关键词  steady- state optimal filtering  self-tuning filtering  deconvolution  smoothing  white noise estimators

中文摘要

      本文用现代时间序列分析方法，提出了基于白噪声估值器和输出预报器解决线性离散定常系统稳态最优和自校正Kalman滤波、预报、去卷、平滑问题新方法。此方法可能应用于跟踪系统、信号处理、通讯系统等领域，仿真例子说明了新方法的有效性。

英文摘要

      Using the modern time series analysis method, based on white noise estimators an output predictors, this paper presents a new approach for solving the steady-state optimal and self-tuning Kalman filtering, prediction and smoothing problems of linear discrete time-invariant systems. The new approach can be applied to the tracking systems, signal processing and communication systems. A simulation example shows its usefulness.