Optimal Balancing of Multi-Function Radar Budget for Multi-Target Tracking Using Lagrangian Relaxation

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Abstract

The radar resource management problem in a multitarget tracking scenario for multi-function radar is considered. To solve it, an optimal balancing of the sensor budget by applying Lagrangian relaxation and the subgradient method is proposed. In a time-invariant scenario it is shown that the proposed method will lead to balanced budgets based on track parameters like maneuverability and measurement uncertainty. Moreover, since real world applications quickly lead to time-varying scenarios, it is demonstrated how the approach can be extended to such cases. Furthermore the proposed method is compared with other budget assignment strategies. This paper is the first step into exploring optimal non-myopic solutions using a POMDP framework for surveillance radar applications involving detection, tracking and classification.
Original languageEnglish
Title of host publication2019 22nd International Conference on Information Fusion (FUSION)
Subtitle of host publicationProceedings
PublisherIEEE
Pages1-8
Number of pages8
ISBN (Electronic)978-0-9964527-8-6
ISBN (Print)978-1-7281-1840-6
Publication statusPublished - 2020
Event22nd International Conference on Information Fusion (FUSION 2019) - Shaw Centre, Ottawa, Canada
Duration: 2 Jul 20195 Jul 2019

Conference

Conference22nd International Conference on Information Fusion (FUSION 2019)
Abbreviated titleFUSION 2019
CountryCanada
CityOttawa
Period2/07/195/07/19

Keywords

  • Radar Resource Management
  • Lagrangian relaxation
  • Steady-state Kalman filter
  • Subgradient method

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