激光与光电子学进展, 2018, 55 (11): 112001, 网络出版: 2019-08-14
基于改进蜻蜓算法的多基地天波雷达定位模型 下载: 865次
Multi-Static Sky-Wave Over-the-Horizon Radar Location Model Based on Improved Dragonfly Algorithm
计算光学 天波超视距雷达 目标定位 极限学习机 蜻蜓优化算法 参数优化 optics in computing sky-wave over-the-horizon radar target location extreme learning machine dragonfly optimization algorithm parameter optimization
摘要
为了提高天波超视距雷达的目标定位精度,提出一种改进蜻蜓算法优化极限学习机的多基地天波超视距雷达目标定位模型。为了避免蜻蜓算法陷入局部最优,将Logistic混沌映射、反向学习策略和变异过程引入蜻蜓算法,形成改进的蜻蜓优化算法;用改进的蜻蜓算法对极限学习机的权值和隐含层偏置进行优化;将优化后的极限学习机应用于多基地天波超视距雷达定位。理论研究和仿真结果表明,该方法能够实现目标的高精度定位,且定位精度和可靠性优于目前常用的天波超视距雷达定位方法和基于误差反向传播神经网络、径向基函数神经网络的目标定位方法,为多基地天波超视距雷达系统提供了一种新的目标定位方法。
Abstract
In order to improve target location accuracy of the sky-wave over-the-horizon radar, a target localization model was proposed based on the improved dragonfly algorithm to optimize the extreme learning machine for the multi-static sky-wave over-the-horizon radar system. Firstly, in order to avoid dragonfly algorithm falling into local optimum, the Logistic chaotic mapping, reverse learning strategy and mutation process are introduced into the dragonfly algorithm to create an improved dragonfly algorithm. Then, the improved dragonfly algorithm is used to optimize the weight and hidden layer bias of the extreme learning machine. Finally, the optimized extreme learning machine is applied to multi-static sky-wave over-the-horizon radar location. Theoretical research and simulation results show that the method can achieve high locating precision of target, and its location accuracy and reliability are better than those of current sky-wave over-the-horizon radar location methods and target location methods based on back propagation neural network and radial basis function neural network. A new target location method is provided for the multi-static sky-wave over-the-horizon radar system.
宋萍, 刘以安. 基于改进蜻蜓算法的多基地天波雷达定位模型[J]. 激光与光电子学进展, 2018, 55(11): 112001. Ping Song, Yian Liu. Multi-Static Sky-Wave Over-the-Horizon Radar Location Model Based on Improved Dragonfly Algorithm[J]. Laser & Optoelectronics Progress, 2018, 55(11): 112001.