Complex Environmental Geomagnetic Matching-Assisted Navigation Algorithm Based on Improved Extreme Learning Machine
Open Access
Abstract
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.Keywords
Funding Information
- National Natural Science Foundation of China (62203191)
This publication has 47 references indexed in Scilit:
- A hybrid optimization method based on extreme learning machine aided factor graph for INS/GPS information fusion during GPS outagesAerospace Science and Technology, 2024
- ELM-based adaptive neuro swarm intelligence techniques for predicting the California bearing ratio of soils in soaked conditionsApplied Soft Computing, 2021
- Performance evaluation of hybrid WOA-XGBoost, GWO-XGBoost and BO-XGBoost models to predict blast-induced ground vibrationEngineering with Computers, 2021
- An efficient equilibrium optimizer with mutation strategy for numerical optimizationApplied Soft Computing, 2020
- A novel hybrid extreme learning machine–grey wolf optimizer (ELM-GWO) model to predict compressive strength of concrete with partial replacements for cementEngineering with Computers, 2020
- A Robust Solution of Integrated SITAN with TERCOM Algorithm: Weight-Reducing Iteration Technique for Underwater Vehicles' Gravity-Aided Inertial Navigation SystemNAVIGATION: Journal of the Institute of Navigation, 2017
- The Whale Optimization AlgorithmAdvances in Engineering Software, 2016
- Grey Wolf OptimizerAdvances in Engineering Software, 2014
- Extreme learning machine and its applicationsNeural Computing and Applications, 2013
- Novel algorithm for geomagnetic navigationJournal of Central South University, 2011