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

计算机科学 跟踪(教育) 人工神经网络 人工智能 期限(时间) 运动(物理) 机器学习 循环神经网络 数据挖掘 心理学 教育学 量子力学 物理
作者
Chang Gao,Junkun Yan,Shenghua Zhou,Pramod K. Varshney,Hongwei Liu
出处
期刊:Information Sciences [Elsevier]
卷期号:502: 279-296 被引量:77
标识
DOI:10.1016/j.ins.2019.06.039
摘要

Target tracking is a difficult estimation problem due to target motion uncertainty and measurement origin uncertainty. In this paper, we consider the target tracking problem in the presence of only target motion uncertainty. The traditional approaches to address this uncertainty, such as multiple model approaches, can suffer performance degradation when there is a model mismatch. The statistical accuracy of conventional model-based methods is also usually limited because of the measurement errors and insufficient data for the estimation. In this paper, deep neural network-based methods are proposed to handle target motion uncertainty due to their strong capability of fitting any mapping as long as there are sufficient training data. Specifically, a recurrent neural network-based structure is proposed to estimate the true states that is consistent with the sequential manner of target tracking. In addition, it is expected that better performance will be achieved due to access to true states during the training of the networks. We propose two networks that are based on different principles and are capable of real-time tracking. An approach to further reduce the computational load is also introduced. Simulation results show that the proposed methods can handle the target motion uncertainty well and provide better estimation accuracy.
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