Time Budget Management in Multifunction Radars Using Reinforcement Learning

Petteri Pulkkinen, Tuomas Aittomaki, Anders Strom, Visa Koivunen

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

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Abstract

An adaptive revisit interval selection (RIS) in multifunction radars is an integral part of efficient time budget management (TBM). In this paper, the RIS problem is formulated as a Markov decision process (MDP) with unknown state transition probabilities and reward distributions. A reward function is proposed to minimize the tracking load (TL) while maintaining the track loss probability (TLP) at a tolerable level. The reinforcement learning (RL) problem is solved using the Q-learning algorithm with an epsilon-greedy policy. Compared to a baseline algorithm, the RL approach was capable of maintaining the tracks while reducing the tracking load significantly.

Original languageEnglish
Title of host publication2021 IEEE Radar Conference
Subtitle of host publicationRadar on the Move, RadarConf 2021
PublisherIEEE
Number of pages6
ISBN (Electronic)9781728176093
DOIs
Publication statusPublished - 7 May 2021
MoE publication typeA4 Article in a conference publication
EventIEEE Radar Conference - Atlanta, United States
Duration: 8 May 202114 May 2021

Publication series

NameProceedings of the IEEE Radar Conference
Volume2021-May
ISSN (Print)1097-5659
ISSN (Electronic)2375-5318

Conference

ConferenceIEEE Radar Conference
Abbreviated titleRadarConf
Country/TerritoryUnited States
CityAtlanta
Period08/05/202114/05/2021

Keywords

  • adaptive update rate
  • Q-learning
  • radar
  • reinforcement learning
  • revisit interval selection
  • time budget management

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