对无人机磁干扰进行智能动态增强补偿
Zizhou Chen1, Zhentao Yu2, Cong Liu2
1Qingdao Innovation and Development Center, Harbin Engineering University, Qingdao 266500, China.
Sensors (Basel, Switzerland)
|August 28, 2025
概括
本研究引入了一种动态增强模型,以提高无人机磁性异常检测的准确性. 这种新方法通过扩展参数和使用基因算法优化的神经网络来显著提高补偿性能, 以更好地描述磁场.
科学领域:
- 地质学
- 航空航天工程
- 信号处理
背景情况:
- 磁干扰显著降低了无人机磁异常检测的准确性.
- 由于参数维度不足,传统的托尔斯-劳森 (T-L) 模型的补偿性能有限.
研究的目的:
- 为无人机磁性异常检测提出一个动态增强的扩展补偿模型.
- 通过扩大参数集来改善磁场的特征.
- 克服线性回归在气磁数据集中的非线性关系的局限性.
主要方法:
- 引入了倾向角和倾向角速率合特征,将参数集从18项扩大到34项.
- 开发了一种基因算法优化的浅反传播神经网络 (GA-BP) 来建模非线性关系.
- 在扩展参数和磁干扰噪声之间建立了高精度的相关性.
主要成果:
- 拟议的模型有效地捕捉了动态飞行态度和干扰场之间的合特性.
- 在磁干扰补偿的关键性能指标中观察到显著的增长.
- 通过扩展参数集实现了磁场的增强特征.
结论:
- 与传统方法相比,这种动态增强模型为无人机提供了更好的磁干扰补偿.
- 这种方法为空中检测系统的抗干扰能力提供了新的优化途径.
- 这项研究为加强无人机气磁测量提供了实用价值.
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