A Track-oriented Approach to Target Tracking with Random Finite Set Observations

Tiancheng Li, Xiaoxu Wang, Yan Liang, Junkun Yan, Hongqi Fan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Scopus citations

Abstract

We have earlier proposed a data-driven approach to target tracking, which models the target movement by using a trajectory function of time (T-FoT) rather than a Markov model. In this work, we extend the approach to account for random finite set observations consisting of both missing and false data. More challenging, the missing and false data are generated under unknown ratios, i.e., they can not be accurately modeled. To tackle this problem, we here propose a data-driven method for identifying the real measurement of the target from clutter if the target is detected and for declaring a misdetection otherwise. Simulation is conducted to demonstrate the effectiveness of our approach, in comparison with the Bayesian-optimal approach.

Original languageEnglish
Title of host publicationICCAIS 2019 - 8th International Conference on Control, Automation and Information Sciences
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728123110
DOIs
StatePublished - Oct 2019
Event8th International Conference on Control, Automation and Information Sciences, ICCAIS 2019 - Chengdu, China
Duration: 23 Oct 201926 Oct 2019

Publication series

NameICCAIS 2019 - 8th International Conference on Control, Automation and Information Sciences

Conference

Conference8th International Conference on Control, Automation and Information Sciences, ICCAIS 2019
Country/TerritoryChina
CityChengdu
Period23/10/1926/10/19

Keywords

  • constrained estimation
  • least squares estimation
  • target tracking
  • Trajectory fitting

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