DeepMTT: A deep learning maneuvering target-tracking algorithm based on bidirectional LSTM network

Abstract

In the field of radar data processing, traditional maneuvering target-tracking algorithms assume that target movements can be modeled by pre-defined multiple mathematical models. However, the changeable and uncertain maneuvering movements cannot be timely and precisely modeled because it is difficult to obtain sufficient information to pre-define multiple models before tracking. To solve this problem, we propose a deep learning maneuvering target-tracking (DeepMTT) algorithm based on a DeepMTT network, which can quickly track maneuvering targets once it has been well trained by abundant off-line trajectory data from existent maneuvering targets. To this end, we first build a LArge-Scale Trajectory (LAST) database to offer abundant off-line trajectory data for network training. Second, the DeepMTT algorithm is developed to track the maneuvering targets using a DeepMTT network, which consists of three bidirectional long short-term memory layers, a filtering layer, a maxout layer and a linear output layer. The simulation results verify that our DeepMTT algorithm outperforms other state-of-the-art maneuvering target-tracking algorithms.