基于改进的适应性H ∞ CKF和IAGA的开采矿车的位置算法研究
Zhen Yang1, Liwen Ji2, Xin Li1
1Faculty of Electrical and Control Engineering, Liaoning Technical University, Huludao, 125105, Liaoning, China.
Scientific reports
|March 17, 2025
概括
这项研究引入了改进的自适应H∞CKF和IAGA-TDOA算法,用于在矿山中准确定位车辆. 新方法显著减少了定位错误,提高了矿山安全和自动化.
科学领域:
- 矿山安全与自动化
- 定位系统 定位系统
- 信号处理 信号处理
背景情况:
- 非煤矿的露天采矿对运输车辆的定位构成了挑战,原因是视线不清的延迟和低精度.
- 现有的定位算法在复杂的采矿环境中扎,影响运营效率和安全.
研究的目的:
- 开发一个改进的适应性H∞CKF和IAGA-TDOA定位算法,用于运输车辆在非煤矿开采矿.
- 构建和验证一个LoRa+4G定位平台,以实现现实世界的有效性.
- 解决非视线延迟问题,提高定位准确性.
主要方法:
- 开发了一种改进的适应性遗传算法 (IAGA),增强了传统的遗传算法,具有适应性交叉和变异率.
- 一个改进的自适应H∞立方卡尔曼波器 (CKF) 算法被设计用于通过优化噪声加权来减轻噪声影响.
- 为实践测试和验证,构建了一个LoRa+4G定位平台.
主要成果:
- 与传统的卡尔曼波器相比,模拟结果显示,改进的自适应H∞CKF算法相对误差减少了85%.
- 与TDOA定位模型的其他四种算法相比,IAGA算法实现了最小的定位误差 (最小0.8m).
- 现场测试证实了平均系统错误在7.4米以内,以及显示历史车辆轨迹的能力.
结论:
- 提议的改进的自适应H∞CKF和IAGA-TDOA算法显著提高了运输车辆在复杂的采矿环境中的定位精度.
- 开发的LoRa+4G平台和算法为提高非煤矿开采矿的运营效率和安全提供了实际解决方案.
- 该系统追踪历史路线的能力为采矿区的自动化生产提供了宝贵的指导.
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