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

Abstract

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