ETRI Journal

  • 제38권5호
  • /
  • Pages.1019-1029
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  • 2016
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  • 1225-6463(pISSN)
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  • 2233-7326(eISSN)
한국전자통신연구원 (Electronics and Telecommunications Research Institute)
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Target Birth Intensity Estimation Using Measurement-Driven PHD Filter

  • Zhang, Huanqing (School of Internet of Things Engineering, Jiangnan University, Key Laboratory of Advanced Process Control for Light Industry (Jiangnan University), Ministry of Education) ;
  • Ge, Hongwei (School of Internet of Things Engineering, Jiangnan University, Key Laboratory of Advanced Process Control for Light Industry (Jiangnan University), Ministry of Education) ;
  • Yang, Jinlong (School of Internet of Things Engineering, Jiangnan University, Key Laboratory of Advanced Process Control for Light Industry (Jiangnan University), Ministry of Education)
  • 투고 : 2016.01.22
  • 심사 : 2016.05.16
  • 발행 : 2016.10.01
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초록

The probability hypothesis density (PHD) filter is an effective means to track multiple targets in that it avoids explicit data associations between the measurements and targets. However, the target birth intensity as a prior is assumed to be known before tracking in a traditional target-tracking algorithm; otherwise, the performance of a conventional PHD filter will decline sharply. Aiming at this problem, a novel target birth intensity scheme and an improved measurement-driven scheme are incorporated into the PHD filter. The target birth intensity estimation scheme, composed of both PHD pre-filter technology and a target velocity extent method, is introduced to recursively estimate the target birth intensity by using the latest measurements at each time step. Second, based on the improved measurement-driven scheme, the measurement set at each time step is divided into the survival target measurement set, birth target measurement set, and clutter set, and meanwhile, the survival and birth target measurement sets are used to update the survival and birth targets, respectively. Lastly, a Gaussian mixture implementation of the PHD filter is presented under a linear Gaussian model assumption. The results of numerical experiments demonstrate that the proposed approach can achieve a better performance in tracking systems with an unknown newborn target intensity.

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피인용 문헌

  1. Target Birth Intensity Estimation in Dense Clutter vol.1792, pp.None, 2021, https://doi.org/10.1088/1742-6596/1792/1/012072