基于PSO_MP算法的电力线信道模型参数识别方法

针对低压电力线信道模型的多参数识别问题,提出了一种基于粒子群优化的匹配追踪算法(particle swarm optimization_ matching pursuit, PSO_MP)。匹配追踪算法(matching pursuit, MP)实现过程简单,但是计算复杂、计算量比较大；粒子群优化算法(particle swarm optimization, PSO)具有全局搜索能力强、收敛快等优点；将两者结合使用不仅可以提高信道模型参数辨识精度,而且还可以提高系统收敛速度。针对传统的高斯原子数目较大的问题,设计了一种新的原子结构。仿真实验表明,基于PSO_MP的模型参数识别精度高,验证了该算法的可行性和优越性。

Parameter identification method of power line channel model based on PSO_MP algorithm

Aiming at the problem of multi parameter identification of low voltage power line channel model, a matching pursuit algorithm based on particle swarm optimization (PSO_ MP).The matching pursuit algorithm (MP) is simple to implement, but it has complex calculation and large amount of calculation; particle swarm optimization (PSO) has the advantages of strong global search ability and fast convergence; the combination of the two can not only improve the accuracy of channel model parameter identification, but also improve the convergence speed of the system. Aiming at the problem of large number of traditional Gaussian atoms, a new atomic structure is designed. The simulation results show that based on PSO_ MP model parameter identification accuracy is high, which verifies the feasibility and superiority of the algorithm.