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The Long Short-Term Memory (LSTM) neural network based data associationalgorithm named as DeepDA for multi-target tracking in clutters is proposed todeal with the NP-hard combinatorial optimization problem in this paper.Different from the classical data association methods involving complex modelsand accurate prior knowledge on clutter density, filter covariance orassociated gating etc, data-driven deep learning methods have been extensivelyresearched for this topic. Firstly, data association mathematical problem formultitarget tracking on unknown target number, missed detection and clutter,which is beyond one-to-one mapping between observations and targets isredefined formally. Subsequently, an LSTM network is designed to learn themeasurement-to-track association probability from radar noisy measurements andexist tracks. Moreover, an LSTM-based data-driven deep neural network after asupervised training through the BPTT and RMSprop optimization method can getthe association probability directly. Experimental results on simulated datashow a significant performance on association ratio, target ID switching andtime-consuming for tracking multiple targets even they are crossing each otherin the complicated clutter environment.

DeepDA: LSTM-based Deep Data Association Network for Multi-Targets Tracking in Clutter