Reinforcement learning based transmitter-receiver selection for distributed MIMO radars

Petteri Pulkkinen, Tuomas Aittomäki, Visa Koivunen

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

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Abstract

Active transmitter-receiver (TX-RX) subset selection facilitates efficient resource use and adaptation to varying target and propagation environments in distributed multiple-input multiple-output (MIMO) radar systems. The problem has been addressed in the literature and objective functions related to radar tasks that depend on the signal-to-interference-plus-noise ratio (SINR) have been proposed. The SINR values observed at the receivers can be estimated assuming a particular propagation environment and target models. In this paper, a novel machine learning approach is proposed in which no such assumptions are needed. We formulate the TX-RX subset selection as a multi-armed bandit (MAB) problem and further extend it to the combinatorial MAB framework. A variety of reinforcement learning algorithms developed for the MAB problem are employed to learn the optimal subset in real-time. It is shown that such algorithms can be effectively used for the TX-RX subset selection problem even in non-stationary scenarios.

Original languageEnglish
Title of host publication2020 IEEE International Radar Conference, RADAR 2020
PublisherIEEE
Pages1040-1045
Number of pages6
ISBN (Electronic)9781728168128
DOIs
Publication statusPublished - Apr 2020
MoE publication typeA4 Article in a conference publication
EventIEEE International Radar Conference - Washington, United States
Duration: 28 Apr 202030 Apr 2020

Conference

ConferenceIEEE International Radar Conference
Abbreviated titleRADAR
CountryUnited States
CityWashington
Period28/04/202030/04/2020

Keywords

  • Distributed MIMO radar
  • Multi-armed bandits
  • Reinforcement learning
  • Subset selection

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