Abstract:
In the field of maneuvering-target tracking (MTT), the targets with changeable and uncertain maneuvering movements cannot be tracked precisely because there always exist ...Show MoreMetadata
Abstract:
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 MT-T 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 ma-neuvering 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.
Published in: ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Date of Conference: 12-17 May 2019
Date Added to IEEE Xplore: 17 April 2019
ISBN Information: