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

Chang Gao, Junkun Yan, Shenghua Zhou, Pramod K. Varshney, Hongwei Liu

Research output: Contribution to journalArticlepeer-review

69 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)279-296
Number of pages18
JournalInformation sciences
StatePublished - Oct 2019
Externally publishedYes


  • Deep neural network
  • Long short-term memory
  • Recurrent neural network
  • Target tracking

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Theoretical Computer Science
  • Computer Science Applications
  • Information Systems and Management
  • Artificial Intelligence


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