基于粒子滤波和滑动平均扩展Kalman滤波的多径估计算法

程兰; 王志远; 陈杰; 谢刚

doi:10.11999/JEIT160587

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

1.
(太原理工大学信息工程学院自动化系太原 030024) ②(北京理工大学复杂系统智能控制与决策国家重点实验室北京 100081)

基金项目:

国家自然科学基金(61503271, 61603267)，山西省自然科学基金(20140210022-7)，复杂系统智能控制与决策国家重点实验室开放基金(900101-03910353)

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出版历程
- 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: 多径干扰是高精度定位的主要误差源，估计多径参数对消除多径误差，提高导航系统定位精度具有重要意义。针对扩展Kalman滤波(EKF)在进行多径参数估计时，存在对初值敏感，估计结果在真值附近具有较大波动的缺点，该文提出一种基于粒子滤波(PF)和滑动平均EKF的多径估计算法。该算法首先利用PF得到多径参数的粗略估计值，并将该值作为EKF的初始估计值，以克服EKF对初值敏感的问题。接着对EKF的估计结果进行滑动平均，并将平均后的滤波结果作为多径参数的估计结果。仿真结果表明，改进后的多径估计算法可有效降低估计结果的波动幅度，同时解决了EKF对初值敏感的问题。
- 参数估计 /
- 粒子滤波 /
- 扩展Kalman滤波 /
- 多径干扰
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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参考文献(24)

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