本文選題：非線性濾波　切入點(diǎn)：貝葉斯濾波　出處：《電子學(xué)報(bào)》2016年01期

【摘要】：反饋粒子濾波器(FPF)是一種連續(xù)時(shí)間最優(yōu)貝葉斯估計(jì)器.針對(duì)FPF對(duì)系統(tǒng)采樣率要求較高的問題,提出一種基于徑向基函數(shù)-Galerkin法求解的反饋粒子濾波器.該算法推導(dǎo)了反饋增益勢(shì)函數(shù)所滿足偏微分方程的弱形式,結(jié)合徑向基函數(shù)對(duì)勢(shì)函數(shù)進(jìn)行近似,利用Galerkin法和蒙特卡羅積分得到了反饋增益近似解,并給出了一種徑向基參數(shù)選取方法,從數(shù)值上分析了徑向基函數(shù)參數(shù)選取對(duì)于濾波精度的影響.仿真算例表明反饋粒子濾波器在低系統(tǒng)采樣率下會(huì)嚴(yán)重發(fā)散,而本文算法能夠避免這一問題,且提高了FPF在低系統(tǒng)采樣率下的濾波精度和穩(wěn)定性.
[Abstract]:Feedback particle filter (FPF) is a continuous-time optimal Bayesian estimator. Aiming at the problem that FPF requires high sampling rate, A feedback particle filter based on Radial basis Function-Galerkin method is proposed, in which the weak form of partial differential equation satisfied by feedback gain potential function is derived, and the potential function is approximated with radial basis function. The approximate solution of feedback gain is obtained by using Galerkin method and Monte Carlo integral, and a radial basis function parameter selection method is given. The influence of radial basis function parameter selection on filtering accuracy is analyzed numerically. The simulation example shows that the feedback particle filter will diverge seriously at low sampling rate, but the algorithm in this paper can avoid this problem. The filtering accuracy and stability of FPF at low sampling rate are improved.
【作者單位】： 海軍工程大學(xué)兵器工程系;
【分類號(hào)】：TN713

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