摘要
传统的信道估计方法往往假设多径分量数已知且为常数,粒子滤波算法可估计服从高斯分布的时变信道。实际无线环境中,多径分量数目与幅度皆为时变,则粒子滤波估计方法性能恶化。本文提出基于二进制粒子群算法和卡尔曼滤波的MIMO-OFDM信道混合估计方法。采用随机集建模MIMO信道,并分析得到其多径转移概率模型;基于此模型将信道分解为离散部分和连续部分,推导得到此两部分与整体信道关系;采用二进制粒子群算法拟合信道离散部分,利用卡尔曼滤波估计信道幅度,将利用信道估计计算得到的观测值与真实观测值的近似程度作为适应度函数。仿真结果表明:本文所提出的信道估计方法性能优于基本粒子滤波的信道估计方法。
The typical estimation methods for MIMO-OFDM channel generally assume that the number of multi-path components is known and constant, and particle filtering can estimate the Gaussian distribution channel. However, in the fact that the number and the amplitude of channel taps are unknown in practical wireless situation, the performance of particle filtering scheme is impaired. In this paper, a channel estimation scheme based on binary PSO and Kalman filtering （PSO-KF） is proposed. Using RST theory a model of MIMO is established and the channel-taps＇ varying model is given. According to the model the whole channel is separated into discrete part and continuous part, and the relationship with them is derived. The discrete part could be obtained using binary PSO algorithm, and the channel amplitude value is acquired using Kalman filtering. The likelihood distance between the real observation and the estimated observation based on the channel estimation is chosen as the fitness function. Simulation results show the performance of PSO-KF algorithm is better than the particle filtering scheme.
出处
《电路与系统学报》
CSCD
北大核心
2012年第2期129-134,共6页
Journal of Circuits and Systems
