Long short-term memory-based recurrent neural networks for nonlinear target tracking

Nonlinear target tracking is essentially to estimate the target state from observations where the system model or observation model undergoes nonlinearities. Among the traditional methods, the particle filter can handle this problem well, though its statistical effectiveness and computational efficiency should be balanced in implementation. In this paper, deep neural network-based methods are proposed to resolve this problem because of their strong capabilities of fitting any mapping as long as the training data is sufficient. Specifically, the long short-term memory-based recurrent neural networks are proposed to take in the observations and output the true states in a sequential manner. Simulation results show that the proposed networks can obtain better estimation accuracy with shorter computational time compared to traditional methods. The newly proposed structure is shown to be able to provide an estimate of the uncertainty related to the target state online and automatically. Besides, nearly the same estimation accuracy can be provided by the proposed methods even when the exact initial prior of the target state is considered unknown. (C) 2019 Elsevier B.V. All rights reserved.