Real-Time Aeromagnetic Compensation With Compressed and Accelerated Neural Networks

Abstract

As neural networks become an increasingly popular technique in the field of aeromagnetic compensation, there is an increasing demand for hardware systems with more computing power. Compared with the linear regression method, applying a neural network to the task of real-time compensation is difficult because of insufficient computing resources in the unmanned aerial vehicle (UAV) flight detection platform. To perform real-time compensation calculations with limited computing resources, we optimized back propagation neural network (OBPNN) through model compression and acceleration. In this study, we found that the most time-consuming part of network training is the iterative updating of the weights in the BPNN interference model. Using transfer learning, we replace the randomly initialized weights (RWs) with pretrained weights, thereby greatly reducing the number of iterations required. We also apply other model compression and acceleration algorithms. In a case study of our new technique, we implement the fast training of the OBPNN on a Raspberry Pi 4B system. This network processes approximately 316 samples per 0.1 s, which is fast enough to complete aeromagnetic compensation in real time.