分離隨機(jī)偏差兩段解耦Wiener濾波器

應(yīng)用現(xiàn)代時(shí)間序列分析方法,基于ARMA 新息模型和Wiener 狀態(tài)濾波器,對(duì)帶隨機(jī)偏差系統(tǒng)首次提出了分離隨機(jī)偏差兩段解耦Wiener 濾波器,形成了一種新的偏差處理技術(shù). 同傳統(tǒng)的兩段Kalman 濾波器相比,具有如下優(yōu)點(diǎn): 1) 可統(tǒng)一處理濾波、平滑和預(yù)報(bào)問題; 2) 避免了Riccati 方程的計(jì)算; 3) 具有最優(yōu)性和漸近穩(wěn)定性; 4)實(shí)現(xiàn)了完全解耦; 5) 便于實(shí)時(shí)應(yīng)用. 兩個(gè)仿真例子說明了其有效性.
關(guān)鍵詞: 隨機(jī)偏差; 輸入偏差; 傳感器偏差; 分離偏差濾波器; 兩段解耦Wiener 濾波器
　　Abstract : Using the modern time series analysis method , based on the ARMA innovation model and Wiener state filters , the separate stochastic bias two- stage decoupled Wiener filters are presented for the first time for systems with stochastic bias ,
which formed a new technique for treatment of bias . Compared to the classical two- stage Kalman filters ,they have the following advantages : 1) they can handle the filtering ,smoothing ,and prediction problems in a unified framework ; 2) the computation of
the Riccati equations is avoided ; 3) they have the optimality and asymptotic stability ; 4) the complete decouple is implemented ; 5) they are suitable for real time applications . Two simulation examples show their effectiveness .
Key words : stochastic bias ; input bias ; sensor bias ; separate bias filters ; two- stage decoupled Wiener filters