This paper proposes a data-driven algorithm

i.e.

a light-weight bi-directional long short-term memory (BiLSTM) network-based intelligent data association between echoes and tracks for multi-radar multi-target tracking

in light of the issue that data association is prone to error and that exact modeling-based algorithms have enormous computational costs for multi-radar multi-target tracking in dense clutter environments. The first step is to build the multi-radar association matrix

whose constituent is the association result between target tracks and radar echoes. Based on multi-radar echoes and predicted measurements

the distance tensor is designed based on max-min normalization. The light-weight BiLSTM networks-based multi-radar multi-target data association network is put forward

by taking the above normalized distance tensor and multi-radar association matrix as the input and output. And the measurement corresponding to the maximum probability is treated as the associated one to update every track through implementing a Kalman filter for each radar. The simulation results of multi-radar tracking multi-target in dense clutter environment show that the association accuracy and tracking precision of the proposed algorithm are similar with those of the centralized joint probability data association filter

which are much better than those of probability data association filter

nearest neighbor data association filter

fully connected layer-based data association filter and long short-term memory (LSTM) networks-based data association filter. Furthermore

compared to the centralized joint probability data association filter

which is nearly equal to the nearest neighbor data association filter

the proposed algrithm’s average running time is significantly shorter.