Huang Jian, Hu Zhe, Yi Wenjun
National Key Laboratory of Transient Physics, Nanjing University of Science and Technology, Nanjing 210094, China.
Sensors (Basel). 2025 Jul 10;25(14):4310. doi: 10.3390/s25144310.
In complex environments where satellite signals may be interfered with, it is difficult to achieve precise positioning of high-speed aerial vehicles solely through the inertial navigation system. To overcome this challenge, this paper proposes an NGO-ELM geomagnetic matching-assisted navigation algorithm, in which the Northern Goshawk Optimization (NGO) algorithm is used to optimize the initial weights and biases of the Extreme Learning Machine (ELM). To enhance the matching performance of the NGO-ELM algorithm, three improvements are proposed to the NGO algorithm. The effectiveness of these improvements is validated using the CEC2005 benchmark function suite. Additionally, the IGRF-13 model is utilized to generate a geomagnetic matching dataset, followed by comparative testing of five geomagnetic matching models: INGO-ELM, NGO-ELM, ELM, INGO-XGBoost, and INGO-BP. The simulation results show that after the airborne equipment acquires the geomagnetic data, it only takes 0.27 µs to obtain the latitude, longitude, and altitude of the aerial vehicle through the INGO-ELM model. After unit conversion, the average absolute errors are approximately 6.38 m, 6.43 m, and 0.0137 m, respectively, which significantly outperform the results of four other models. Furthermore, when noise is introduced into the test set inputs, the positioning error of the INGO-ELM model remains within the same order of magnitude as those before the noise was added, indicating that the model exhibits excellent robustness. It has been verified that the geomagnetic matching-assisted navigation algorithm proposed in this paper can achieve real-time, accurate, and stable positioning, even in the presence of observational errors from the magnetic sensor.
在卫星信号可能受到干扰的复杂环境中，仅通过惯性导航系统很难实现高速飞行器的精确定位。为克服这一挑战，本文提出一种非政府组织-极限学习机（NGO-ELM）地磁匹配辅助导航算法，其中使用苍鹰优化（NGO）算法优化极限学习机（ELM）的初始权重和偏置。为提高NGO-ELM算法的匹配性能，对NGO算法提出了三项改进。使用CEC2005基准函数套件验证了这些改进的有效性。此外，利用国际地磁参考场（IGRF）-13模型生成地磁匹配数据集，随后对五种地磁匹配模型进行对比测试：INGO-ELM、NGO-ELM、ELM、INGO-极端梯度提升（XGBoost）和INGO-反向传播（BP）。仿真结果表明，机载设备获取地磁数据后，通过INGO-ELM模型仅需0.27微秒即可获得飞行器的纬度、经度和高度。经过单位换算后，平均绝对误差分别约为6.38米、6.43米和0.0137米，显著优于其他四种模型的结果。此外，当在测试集输入中引入噪声时，INGO-ELM模型的定位误差与添加噪声之前处于同一数量级，表明该模型具有出色的鲁棒性。已经验证，本文提出的地磁匹配辅助导航算法即使在存在磁传感器观测误差的情况下，也能实现实时、准确和稳定的定位。
