Automatic Recognition of Geomagnetic Suitability Areas for Path Planning of Autonomous Underwater Vehicle

Abstract

Currently, integrated navigation systems with the inertial navigation system (INS)/geomagnetic navigation system (GNS) have been widely used in underwater navigation of autonomous underwater vehicle (AUV). Restricting AUV to navigate in the geomagnetic suitability areas (GSA) as far as possible can effectively improve the accuracy of integrated navigation systems. In order to improve the classification accuracy of GSA, a new optimal classification method based on principal component analysis (PCA) and improved back propagation (BP) neural network is proposed. PCA is used to extract the independent characteristic parameters containing the main components. Then, considering similarity coefficient, the initial weights and thresholds of BP neural network is optimized by improved adaptive genetic algorithm (IAGA). Finally, the correspondence between the geomagnetic characteristic parameters and matching performance is established based on PCA and improved adaptive genetic algorithm and back propagation (IAGA-BP) neural network for the automatic recognition of GSA. Simulated experiments based on PCA and IAGA-BP neural network shows high classification accuracy and reliability in the GSA selection. The method could provide important support for AUV path planning, which is an effective guarantee for AUV high precision and long voyage autonomous navigation.

Keywords:

• Automatic recognition
• autonomous underwater vehicle
• bp neural network
• geomagnetic suitability areas
• improved adaptive genetic algorithm
• integrated navigation systems
• principal component analysis

Acknowledgments

Thanks to NGDC for providing the EMAG2 v3.

Additional information

Funding

This research was funded by the National Nature Science Foundation of China (42074014, 42074006, 41774021, 41774037, 41874016, 41876103 and 41904039) and State Key Laboratory of Geo-information Engineering (SKLGIE 2017-M-2-6).