An improved dynamic programming tracking-before-detection algorithm based on LSTM network

Abstract The detection and tracking of small weak maneuvering radar targets in complex electromagnetic environments is still a difficult problem to effectively solve. To address this problem, paper proposes dynamic programming tracking-before-detection method based on long short-term memory (LSTM) network (LSTM-DP-TBD). With the predicted target motion state provided by LSTM network, transition range traditional DP-TBD algorithm can be updated real time, effect achieved for also improved. Utilizing model moving target, features learned from noisy input data. By incorporating these into algorithm, set adjusted time with changes so that new capable recursively accumulating movement trend target. Simulation results show able accomplish task detecting targets, it achieves improved probabilities.