For the traditional target localization algorithms of frequency diverse array (FDA), there are some problems such as angle and distance coupling in single-frequency receiving FDA mode, large amount of calculation, and weak adaptability. This paper introduces a good learning and predictive method of target localization by using BP neural network on FDA, and FDA-IPSO-BP neural network algorithm is formed. The improved particle swarm optimization (IPSO) algorithm with nonlinear weights is developed to optimize the neural network weights and biases to prevent BP neural network from easily falling into local minimum points. In addition, the decoupling of angle and distance with single frequency increment is well solved. The simulation experiments show that the proposed algorithm has better target localization effect and convergence speed, compared with FDA-BP and FDA-MUSIC algorithms.

中文翻译：

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基于IPSO-BP的分频阵列目标定位

对于传统的频分阵列（FDA）目标定位算法，存在单频接收FDA模式下的角度和距离耦合，计算量大，适应性较弱等问题。通过在FDA上使用BP神经网络介绍了一种很好的目标定位学习和预测方法，并形成了FDA-IPSO-BP神经网络算法。开发了具有非线性权重的改进粒子群优化（IPSO）算法，以优化神经网络权重和偏差，以防止BP神经网络轻易陷入局部最小点。另外，很好地解决了角度和距离与单个频率增量的解耦问题。仿真实验表明，该算法具有较好的目标定位效果和收敛速度，