Frequency Diverse Array Target Localization Based on IPSO-BP

Frequency Diverse Array Target Localization Based on IPSO-BP
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基于IPSO-BP的频率分集阵列目标定位

DOI:
10.1155/2020/2501731
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发表时间:
2020-08
影响因子:
1.5
通讯作者:
谢全明
谢全明
中科院分区:
计算机科学4区
文献类型:
--
作者:
刘庆华;丁凯;吴丙森;谢全明

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传统的频率分集阵列(FDA)目标定位算法在单频接收FDA模式下存在角度和距离耦合、计算量大、适应性差等问题。本文介绍了一种利用BP神经网络对FDA进行目标定位的学习和预测方法,并形成了FDA-IPSO-BP神经网络算法。针对BP神经网络容易陷入局部极小点的问题,提出了一种带非线性权值的改进粒子群优化算法(IPSO)来优化神经网络的权值和偏差。此外,较好地解决了单频增量下的角度和距离解耦问题。仿真实验表明,与FDA-BP和FDA-MUSIC算法相比,该算法具有更好的目标定位效果和收敛速度。
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.
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