基于粒子濾波和滑動平均擴展Kalman濾波的多徑估計算法

程蘭; 王志遠; 陳杰; 謝剛

doi:10.11999/JEIT160587

基于粒子濾波和滑動平均擴展Kalman濾波的多徑估計算法

doi: 10.11999/JEIT160587

1.
(太原理工大學信息工程學院自動化系太原 030024) ②(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北京 100081)

基金項目:

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)

計量
- 文章訪問數(shù): 1580
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- 被引次數(shù): 0
出版歷程
- 收稿日期: 2016-06-03
- 修回日期: 2016-11-11
- 刊出日期: 2017-03-19

An Improved Multipath Estimation Algorithm Using Particle Filter and Sliding Average Extended Kalman Filter

1.
(College of Information Engineering, Taiyuan University of Technology, Taiyuan 030024, China)
2.
(State Key Laboratory of Intelligent Control and Decision of Complex Systems, Beijing Institute of Technology, Beijing 100081, China)

Funds:

The National Natural Science Foundation of China (61503271, 61603267), The Natural Science Foundation of Shanxi Province (20140210022-7), The Open Foundation of Key Laboratory of Intelligent Control and Decision of Complex Systems (900101-03910353)

摘要

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。
- 參數(shù)估計 /
- 粒子濾波 /
- 擴展Kalman濾波 /
- 多徑干擾
Abstract: Multipath is the dominant error source for high-accuracy positioning systems. It is significant for eliminating the multipath error and improving the positioning accuracy to estimate multipath parameters. There are two main disadvantages for multipath parameters estimation by using the Extended Kalman Filter (EKF): it is sensitive to the initial value; filtering results fluctuate obviously around actual values. To solve these problems, an improved multipath estimation algorithm based on Particle Filter (PF) and sliding average EKF is proposed. Firstly, PF is used to obtain rough estimation values of multipath parameters, which are set as initial estimations for EKF to reduce the initial value sensitivity. Then, the EKF filtering results are smoothed by sliding average. The smoothing results are outputted as the multipath estimation. The simulation results show that the estimation results of the proposed algorithm have smaller fluctuation magnitude compared with EKF, and it is insensitive to the initial estimation.
- Parameter estimation /
- Particle Filter (PF) /
- Extended Kalman Filter (EKF) /
- Multipath interference

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參考文獻(24)

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