An Automatic Search and Energy-Saving Continuous Tracking Algorithm for Underwater Targets Based on Prediction and Neural Network

Abstract

Underwater target search and tracking has become a technical hotspot in underwater sensor networks (UWSNs). Unfortunately, the complex and changeable marine environment creates many obstacles for localization and tracking. This paper proposes an automatic search and energy-saving continuous tracking algorithm for underwater targets based on prediction and neural network (ST-BPN). Firstly, the network contains active sensor nodes that can transmit detection signal. When analyzing the reflected signal spectrum, a modified convolutional neural network M-CNN is built to search the target. Then, based on the relationship between propagation delay and target location, a localization algorithm which can resist the influence of clock asynchrony LA-AIC is designed. Thirdly, a scheme based on consensus filtering TS-PSMCF is used to track the target. It is worth mentioning that a predictive switching mechanism, PSM, is added to the tracking process to adjust the working state of nodes. Simulation results show that the recognition accuracy of M-CNN is as high as 99.7%, the location accuracy of LA-AIC is 92.3% higher than that of traditional methods, and the tracking error of TS-PSMCF is kept between 0 m and 5 m.