Data association for maneuvering targets through a combined siamese network and XGBoost model

Chang Gao, Junkun Yan, Bo Chen, Pramod K. Varshney, Tianyi Jia, Hongwei Liu

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


Data association plays an important role in forming target tracks when false alarms exist. Its accuracy is key to reducing the computational burden of the combinatorial explosion problem inherent to target tracking in environments where false alarms are densely distributed. Traditional methods are usually developed according to some assumed motion models and suffer performance degradation when there is mismatch between assumed models and the actual motion, which may often be the case for maneuvering targets. To cope with this problem, we propose a data-driven method that learns the association criteria directly from data. By use of the domain knowledge and a convolutional Siamese network, features of different attributes present in the observations are extracted. Then the features are fused through the use of XGBoost. Simulation results show that the proposed method performs better than the traditional model-based methods in correlating observations of maneuvering targets and avoiding correlations of false alarms. By visualizing the decision process of the feature fusion model, it is found that the proposed method learns from the data to make comprehensive use of multiple features and does not simply set hard thresholds for the features. The computational complexity is also analyzed both theoretically and experimentally.

Original languageEnglish (US)
Article number109086
JournalSignal Processing
StatePublished - Oct 2023
Externally publishedYes


  • Data association
  • Siamese neural network
  • Target tracking
  • XGBoost

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering


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