一种新型混合并行粒子滤波频率估计方法

doi:10.3969/j.issn.0372-2112.2016.03.036

电子学报 ›› 2016, Vol. 44 ›› Issue (3): 740-746.DOI: 10.3969/j.issn.0372-2112.2016.03.036

一种新型混合并行粒子滤波频率估计方法

王伟, 余玉揆, 郝燕玲

哈尔滨工程大学自动化学院, 哈尔滨, 150001

收稿日期:2014-06-25 修回日期:2015-05-20 出版日期:2016-03-25
- 通讯作者:
- 王伟
- 作者简介:
- 余玉揆 男,1989年5月出生于江西抚州,哈尔滨工程大学自动化学院博士研究生.主要研究方向为非线性滤波,光纤非线性,光通讯信号处理. E-mail:yykmaidou@gmail.com;郝燕玲 女,1944年1月出生于山东省掖县,哈尔滨工程大学自动化学院教授,主要研究方向为组合导航技术、惯性导航及定位技术. E-mail:ylhao@sina.com
- 基金资助:
- 国家自然科学基金 (No.61571148); 中国博士后科学基金 (No.2014M550182); 黑龙江省博士后特别资助 (No.LBH-TZ0410); 哈尔滨市科技创新人才资助课题 (No.2013RFXXJ016)

A Novel Parallel Particle Filter for Frequency Estimation

WANG Wei, YU Yu-kui, HAO Yan-ling

Harbin Engineering University, College of Automation, Harbin, Heilongjiang 150001, China

Received:2014-06-25 Revised:2015-05-20 Online:2016-03-25 Published:2016-03-25

摘要/Abstract

摘要：

针对高动态、低信噪比环境下的载波频率信号跟踪问题,提出一种新的混合并行粒子滤波算法(Multiple Extend Kalman Filter Independent Metropolis Hastings,M-E-IMH).该算法具有并行运算结构,实时性较基本粒子滤波有较大的提高.该算法直接利用同相支路(In-phase,I)和正交支路(Quadrature,Q)作为观测量,避免了传统方法中的鉴别器引入而引起的信噪比损耗.在高斯和非高斯环境下,与现有的载波跟踪方法如扩展卡尔曼滤波器(EKF),粒子滤波器(PF),卡尔曼滤波器(KF)等仿真对比表明,该方法在低信噪比下具有更高的跟踪精度.

关键词: 多普勒频率估计, 并行粒子滤波, 高动态, 非高斯噪声, 实时性

Abstract:

To improve the tracking accuracy of the carrier frequency in low signal-to-noise ratio (SNR) and high dynamic environment,a new hybrid parallel particle filter algorithm,named multiple extend Kalman filter independent metropolis hastings (M-E-IMH) is presented.The proposed algorithm has a parallel structure and is verified to be more efficient for the real time implementation compared with particle filter (PF).The method utilizes the output of the in-phase and quadrature (IQ) branch as the observation directly to avoid the SNR loss caused by the discriminator.In both guass and non-guass environment,the simulations show that the proposed method has higher tracking accuracy at low SNR compared with the traditional methods,such as extended Kalman filter (EKF),particle filter (PF) and Kalman filter (KF) etc.

Key words: Doppler frequency estimation, parallel particle filter, high dynamic, non Gauss noise, real-time

中图分类号:

TN966

王伟, 余玉揆, 郝燕玲. 一种新型混合并行粒子滤波频率估计方法[J]. 电子学报, 2016, 44(3): 740-746.

WANG Wei, YU Yu-kui, HAO Yan-ling. A Novel Parallel Particle Filter for Frequency Estimation[J]. Acta Electronica Sinica, 2016, 44(3): 740-746.

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一种新型混合并行粒子滤波频率估计方法

A Novel Parallel Particle Filter for Frequency Estimation

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