本文選題：參數(shù)估計　切入點：粒子濾波　出處：《電子與信息學報》2017年03期 　論文類型：期刊論文

【摘要】：多徑干擾是高精度定位的主要誤差源,估計多徑參數(shù)對消除多徑誤差,提高導航系統(tǒng)定位精度具有重要意義。針對擴展Kalman濾波(EKF)在進行多徑參數(shù)估計時,存在對初值敏感,估計結果在真值附近具有較大波動的缺點,該文提出一種基于粒子濾波(PF)和滑動平均EKF的多徑估計算法。該算法首先利用PF得到多徑參數(shù)的粗略估計值,并將該值作為EKF的初始估計值,以克服EKF對初值敏感的問題。接著對EKF的估計結果進行滑動平均,并將平均后的濾波結果作為多徑參數(shù)的估計結果。仿真結果表明,改進后的多徑估計算法可有效降低估計結果的波動幅度,同時解決了EKF對初值敏感的問題。
[Abstract]:Multipath interference is the main error source of high precision positioning. It is important to estimate multipath parameters to eliminate multipath errors and to improve positioning accuracy of navigation system. The extended Kalman filter is sensitive to initial values when it is used to estimate multipath parameters. This paper presents a multipath estimation algorithm based on particle filter (PF) and moving average EKF (EKF). Firstly, a rough estimation of multipath parameters is obtained by using PF. In order to overcome the problem that EKF is sensitive to the initial value, this value is taken as the initial estimation value of EKF. Then, the moving average of the EKF estimation result is carried out, and the average filtering result is taken as the estimation result of the multipath parameter. The simulation results show that, The improved multipath estimation algorithm can effectively reduce the fluctuation of the estimation results and solve the problem that EKF is sensitive to the initial value.
【作者單位】： 太原理工大學信息工程學院自動化系;北京理工大學復雜系統(tǒng)智能控制與決策國家重點實驗室;
【基金】：國家自然科學基金(61503271,61603267) 山西省自然科學基金(20140210022-7) 復雜系統(tǒng)智能控制與決策國家重點實驗室開放基金(900101-03910353)~~
【分類號】：TN713
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本文編號：1641618