  The Long Short-Term Memory (LSTM) neural network based data association
algorithm named as DeepDA for multi-target tracking in clutters is proposed to
deal with the NP-hard combinatorial optimization problem in this paper.
Different from the classical data association methods involving complex models
and accurate prior knowledge on clutter density, filter covariance or
associated gating etc, data-driven deep learning methods have been extensively
researched for this topic. Firstly, data association mathematical problem for
multitarget tracking on unknown target number, missed detection and clutter,
which is beyond one-to-one mapping between observations and targets is
redefined formally. Subsequently, an LSTM network is designed to learn the
measurement-to-track association probability from radar noisy measurements and
exist tracks. Moreover, an LSTM-based data-driven deep neural network after a
supervised training through the BPTT and RMSprop optimization method can get
the association probability directly. Experimental results on simulated data
show a significant performance on association ratio, target ID switching and
time-consuming for tracking multiple targets even they are crossing each other
in the complicated clutter environment.
