基于粒子滤波和滑动平均扩展Kalman滤波的多径估计算法

doi:10.11999/JEIT160587

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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.

Key words： Parameter estimation Particle Filter (PF) Extended Kalman Filter (EKF) Multipath interference

Received: 03 June 2016 Published: 22 January 2017

PACS:	TN966
	TP273.1

Fund:

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)

Corresponding Authors: CHENG Lan E-mail: taolan_1983@126.com

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	CHENG Lan
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Cite this article:

CHENG Lan,WANG Zhiyuan,CHEN Jie等. An Improved Multipath Estimation Algorithm Using Particle Filter and Sliding Average Extended Kalman Filter[J]. JEIT, 2017, 39(3): 709-716.

URL:

http://jeit.ie.ac.cn/EN/10.11999/JEIT160587 OR http://jeit.ie.ac.cn/EN/Y2017/V39/I3/709

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