A DEEP NEURAL NETWORK BASED MANEUVERING-TARGET TRACKING ALGORITHM

In the field of maneuvering-target tracking (MTT), the targets with changeable and uncertain maneuvering movements cannot be tracked precisely because there always exist time delays of maneuvering model estimation with traditional MTT algorithms. To solve this problem, we propose a deep MTT (DeepMTT) algorithm based on a deep neural 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 database to offer abundant off-line trajectory data for network training. Second, the DeepMTT algorithm is developed based on a deep neural 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 MTT algorithms.