AUV Drift Track Prediction Method Based on a Modified Neural Network

Abstract

Since AUV works in the complex marine environment without cable and unmanned, there will be a loss of contact when an accident occurs. It is necessary to carry out research on the drift track prediction of AUV for the sake of salvage and recovery of the AUV in time. It is worth noting that the volume of AUV is small, and the drift track changes significantly when it is affected by the marine environment. Consequently, when the AUV drifts to different ocean layers, there will be a feature drift problem which will lead to a significant drop in the prediction accuracy. In this paper, a new method of AUV drift track prediction is proposed. Inspired by the human emotion modulation mechanism in psychology, a modified neural network (ECRNet) is proposed to correct the prediction error in different ocean layers. Through experimental verification, the network reduces the prediction error and achieves a better prediction performance.