基于粒子群优化匹配追踪算法的电力线信道模型参数识别方法

Parameter Identification Method of Power Line Channel Model Based on Particle Swarm Optimization Matching Pursuit Algorithm

为了提高低压电力线信道多径模型参数识别精度,同时减少参数识别时间,通过将粒子群优化(particle swarm optimization,PSO)算法和匹配追踪(matching pursuit,MP)算法相融合提出了PSO-MP混合算法,并利用此方法研究了低压电力线信道模型参数识别问题。结果表明:PSO-MP混合算法融合了两者互补的优势,基于该混合算法的信道参数识别精度高于分别利用遗传算法(genetic algorithm,GA)、PSO算法、遗传匹配追踪(genetic algorithm-matching pursuit,GA-MP)算法的识别精度,并且该精度会随着路径数目的增多而提高;同时,PSO-MP混合算法不仅提高了信道模型参数识别精度,还提高了系统收敛速度。可见,基于PSO-MP混合算法的低压电力线多径模型参数识别方法可行、高效。

In order to improve the parameter identification accuracy of low-voltage power line channel multipath model and reduce the parameter identification time,PSO-MP hybrid algorithm was proposed by combining particle swarm optimization(PSO)algorithm and matching pursuit(MP)algorithm,PSO-MP hybrid algorithm was used to study the parameter identification of low-voltage power line channel model.The results show that the channel parameter identification accuracy based on the hybrid algorithm is higher than that of genetic algorithm(GA),PSO algorithm and genetic algorithm matching pursuit(GA-MP)algorithm respectively,and the accuracy will improve with the increase of the number of paths.At the same time,PSO-MP hybrid algorithm not only improves the accuracy of channel model parameter identification,but also improves the convergence speed of the system.It is concluded that the parameter identification method of low-voltage power line multipath model based on PSO-MP hybrid algorithm is feasible and efficient.