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Open Access
 Issue JNWPU Volume 39, Number 1, February 2021 77 - 84 https://doi.org/10.1051/jnwpu/20213910077 09 April 2021

© 2021 Journal of Northwestern Polytechnical University. All rights reserved.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

1 无人机全区域侦察数学模型

1.2 无人机模型

 图1无人机等高盘旋受力图

1.3 评价指标

1) 覆盖率

Sc表示已经侦察过的面积, St表示要求侦察的总面积, 则覆盖率

2) 路径重复率

3) 高频重复率

Sh表示覆盖次数高于某一阈值的面积, Sr表示已经侦察的面积, 则高频重复率。其中阈值由飞机最大飞行速度和栅格边长共同决定。高频重复率能够有效衡量算法跳出局部最优的能力。

2 神经网络下的无人机全区域侦察路径规划

2.1 神经网络的构建

 图2S型曲线

2.1.2 输入层d1

 图3机载雷达探测威胁读数
 图4雷达探测到访信息读数

2.1.3 隐藏层及神经元个数的确定

 图5神经网络结构图

2.2 神经网络的无监督学习模型

 图6无监督学习模型

3 仿真实现及结果分析

3.1 仿真系统说明

 图7仿真系统结构图
 图8离线学习过程
 图9最高与平均适应度得分变化曲线
 图10加速优化模式下适应度分数的变化曲线
 图11离线学习模式下适应度分数的变化曲线
 图12简单环境下的在线应用
 图13复杂环境下的在线应用

3.2 仿真结果分析

3.2.2 算法通用性分析

 图14不同覆盖率下的路径重复率变化曲线

References

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9. Ren Q, Li M, Han S. Discrimination and comparison experiments of basalt tectonic setting based on improved genetic algorithm-optimized neural network[J]. Earth Science Frontiers, 2019, 26(4): 117–124 [Article] [Google Scholar]

All Figures

 图1无人机等高盘旋受力图 In the text
 图2S型曲线 In the text
 图3机载雷达探测威胁读数 In the text
 图4雷达探测到访信息读数 In the text
 图5神经网络结构图 In the text
 图6无监督学习模型 In the text
 图7仿真系统结构图 In the text
 图8离线学习过程 In the text
 图9最高与平均适应度得分变化曲线 In the text
 图10加速优化模式下适应度分数的变化曲线 In the text
 图11离线学习模式下适应度分数的变化曲线 In the text
 图12简单环境下的在线应用 In the text
 图13复杂环境下的在线应用 In the text
 图14不同覆盖率下的路径重复率变化曲线 In the text

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