一种新型混合并行粒子滤波频率估计方法

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（ Multi-ple 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。

A Novel Parallel Particle Filter for Frequency Estimation

To improve the tracking accuracy of the carrier frequency in low signal-to-noise ratio ( SNR) and high dy-namic 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 envi-ronment,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.