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 language | English |
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Title of host publication | 2020 IEEE International Radar Conference, RADAR 2020 |
Publisher | IEEE |
Pages | 1040-1045 |
Number of pages | 6 |
ISBN (Electronic) | 9781728168128 |
DOIs | |
Publication status | Published - Apr 2020 |
MoE publication type | A4 Article in a conference publication |
Event | IEEE International Radar Conference - Washington, United States Duration: 28 Apr 2020 → 30 Apr 2020 |
Conference
Conference | IEEE International Radar Conference |
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Abbreviated title | RADAR |
Country | United States |
City | Washington |
Period | 28/04/2020 → 30/04/2020 |
Keywords
- Distributed MIMO radar
- Multi-armed bandits
- Reinforcement learning
- Subset selection