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

CHENG Lan; WANG Zhiyuan; CHEN Jie; XIE Gang

doi:10.11999/JEIT160587

Volume 39 Issue 3

Mar. 2017

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Article Navigation > Journal of Electronics & Information Technology > 2017 > 39(3): 709-716

CHENG Lan, WANG Zhiyuan, CHEN Jie, XIE Gang. An Improved Multipath Estimation Algorithm Using Particle Filter and Sliding Average Extended Kalman Filter[J]. Journal of Electronics & Information Technology, 2017, 39(3): 709-716. doi: 10.11999/JEIT160587

Citation:

CHENG Lan, WANG Zhiyuan, CHEN Jie, XIE Gang. An Improved Multipath Estimation Algorithm Using Particle Filter and Sliding Average Extended Kalman Filter[J]. Journal of Electronics & Information Technology, 2017, 39(3): 709-716. doi: 10.11999/JEIT160587

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

doi: 10.11999/JEIT160587

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)

Received Date: 2016-06-03
Rev Recd Date: 2016-11-11
Publish Date: 2017-03-19

Abstract

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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References(24)

References

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