基于改进ALO-RBF的高频地波雷达海杂波预测模型

Sea clutter prediction model of high frequency surface wave radar based on improved ALO-RBF neural network

摘要:

高频地波雷达是海上动目标检测的重要手段，其中海杂波是影响海面目标检测性能的主要因素。为了提高海杂波的预测精度进而有效抑制海杂波，本文提出了一种基于改进蚁狮算法（Ant Lion Optimizer，ALO）优化RBF神经网络的海杂波预测模型（MGPALO-RBF，Multiple elites dynamic guidance Ant Lion Optimizer based on Gaussian difference variation-based learning with Perturbation factor-radial basis function）。由于标准蚁狮算法具有易陷入局部最优且收敛速度慢的缺点，本文在蚂蚁进行随机行走的过程中加入扰动因子以增加种群的活跃性和多样性，并提出多个精英动态引导机制，强化算法前期的探索能力和后期的开发能力，同时对种群中较差蚁狮进行高斯差分变异以提高算法的收敛速度。仿真结果表明：改进的蚁狮算法在对比算法中具有更高的收敛精度和收敛速度，MGPALO-RBF模型具有更好的海杂波预测性能。

Abstract:

High-frequency ground wave radar is an important means of detecting moving targets on the sea, among which sea clutter is the primary factor that influences the function of sea surface target detection. For the sake of enhancing the prediction precision of sea clutter and validly restrain the sea clutter, this paper proposes a sea clutter prediction model (MGPALO-RBF) based on the improved ant lion algorithm to optimize the RBF neural network. In order to ameliorate the weak point of the ant lion algorithm that it sink easily into the local optimum and the convergence velocity is slow, a disturbance factor is added to increase the activity and variety of the population during the process of stochastic walk of the ants; multiple elite dynamic guidance mechanisms are put forward to intensify the searching capability of the algorithm in the prophase and the exploit ability in the later stage; Gaussian difference mutation is put forward on the poor ant lion in the population to enhance the convergence velocity of the algorithm. The simulation results make clear that the mended ant lion algorithm has higher convergence precision and constriction speed in the contrast algorithm, and the optimized model has a better effect in sea clutter prediction.