基于改进粒子群优化的广义K分布杂波模型参数估计方法

摘要

In the modeling, simulation and classification of the clutter, the estimation of model parameters of the clutter is an important research area. For the frequently adopted generalized K-distribution clutter model,the speckle and amplitude modulation components are both assumed to obey the generalized Gamma distribution. It turns out that the parameter estimation in this model is difficult due to high-dimensionality and nonlinearity. In order to solve this problem, this paper applies the improved particle swarm optimization (PSO) to the estimation of parameters of the generalized K-distribution. Specifically, the paper adopts the uniform design method to initialize the particle swarm and employs the strategy of across and mutation to improve the global convergence performance of the standard PSO. In fact, the proposed method can accurately estimate each parameter of the clutter model. Moreover, the method has the advantages of low computation burden, fast convergence rate and preferable stability. It is demonstrated by simulation results that the method is of good adaptability and estimation accuracy, which proves its effectiveness and exactness.%在杂波建模、仿真和分类识别研究中,杂波模型参数估计是一个重要的内容.广义K分布杂波模型的散斑分量和幅度调制分量均服从广义Gamma分布,参数估计存在高维、非线性等问题.将改进的粒子群优化算法应用于广义K分布杂波模型参数估计,采用均匀设计方法初始化粒子群,利用交叉变异策略改善粒子群优化的全局收敛性,该方法能准确地估计杂波模型各参数,计算简单,收敛速度较快,稳定性较好.仿真实验结果表明该方法具有良好的适应性和估计精度,验证了其有效性和准确性.