电子学报 ›› 2018, Vol. 46 ›› Issue (1): 167-174.DOI: 10.3969/j.issn.0372-2112.2018.01.023

• 学术论文 • 上一篇    下一篇

基于ε等级约束差分进化的多径估计算法

程兰1, 邢艳君1, 任密蜂1, 谢刚1, 陈杰2   

  1. 1. 太原理工大学信息工程学院, 山西太原 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
  • 收稿日期:2016-04-19 修回日期:2016-12-07 出版日期:2018-01-25
    • 作者简介:
    • 程兰,女,1984年3月出生,河南光山人.2005年、2008年获太原理工大学信息工程学院学士、硕士学位,2012年获北京理工大学自动化学院控制科学与工程专业博士学位.现为太原理工大学信息工程学院讲师,主要从事状态估计理论、优化理论、导航系统高精度定位等研究.E-mail:taolan_1983@126.com;邢艳君,女,1992年7月出生,山西介休人.2015年毕业于天津科技大学自动化系,同年进入太原理工大学控制科学与工程系.现为硕士研究生,主要从事状态估计理论、优化理论、导航系统高精度定位等研究.E-mail:xingyanj135@qq.com
    • 基金资助:
    • 国家自然科学基金 (No.61603267,No.61503271); 山西省自然科学基金 (No.20140210022-7)

Multipath Estimation Algorithm Using ε Constrained Rank-Based Differential Evolution

CHENG Lan1, XING Yan-jun1, REN Mi-feng1, XIE Gang1, CHEN Jie2   

  1. 1. College of Information Engineering, Taiyuan University of Technology, Taiyuan, Shanxi 030024, China;
    2. State Key Laboratory of Intelligent Control and Decision of Complex Systems, Beijing Institute of Technology, Beijing 100081, China
  • Received:2016-04-19 Revised:2016-12-07 Online:2018-01-25 Published:2018-01-25
    • Supported by:
    • National Natural Science Foundation of China (No.61603267, No.61503271); Natural Science Foundation of Shanxi Province,  China (No.20140210022-7)

摘要: 本文针对基于扩展Kalman滤波(EKF)的多径估计算法需要对非线性观测方程进行线性化.对初值比较敏感,造成估计性能下降的问题,提出了基于智能优化的多径估计算法.该算法将估计误差的二阶矩作为目标函数,将瞬时误差作为约束条件,同时考虑多径参数的先验信息,实现了将多径估计问题转化为具有约束条件的优化问题.然后,利用一种智能优化算法来解决该优化问题.本文采用了ε等级约束差分进化(εCRDE)算法来解决有约束条件的优化问题,并对该算法进行改进,使改进后的εCRDE算法可以实现多径参数的迭代估计.仿真结果表明,与EKF算法相比,在单一多径和2路多径情况下,基于改进εCRDE的多径估计算法都具有更好的估计性能.

关键词: 多径估计, 优化算法, 差分进化(DE), Kalman滤波

Abstract: The observation equation has to be linearized for the multipath estimation algorithm based on Extended Kalman Filter (EKF).To tackle the problem of being sensitive to initial state, which leads to a performance degradation in terms of estimation accuracy, a new multipath estimation algorithm based on intelligent optimization is proposed.Through minimizing the second moment of the estimation error the multipath estimation problem is transferred into an optimization problem with constrained conditions.Furthermore, the instantaneous error is considered as a constrained condition as well as the prior information of the multipath parameters.Then, an intelligent optimization algorithm is used to solve the presented optimization problem.Especially, the ε Constrained Rank-based Differential Evolution (εCRDE) algorithm is adopted.In addition, the εCRDE algorithm is improved to cater for the need of iteration for multipath estimation.Simulation results show that the proposed algorithm outperforms EKF for multipath estimation in the case of single multipath and two multipaths.

Key words: multipath estimation, optimization algorithm, differential evolution (DE), Kalman filter

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