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Reinforcement learning based transmitter-receiver selection for distributed MIMO radars
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A4 Artikkeli konferenssijulkaisussa
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en
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6
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2020 IEEE International Radar Conference, RADAR 2020, pp. 1040-1045
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.
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Pulkkinen, P, Aittomäki, T & Koivunen, V 2020, Reinforcement learning based transmitter-receiver selection for distributed MIMO radars. in 2020 IEEE International Radar Conference, RADAR 2020., 09114644, IEEE, pp. 1040-1045, IEEE Radar Conference, Washington, District of Columbia, United States, 28/04/2020. https://doi.org/10.1109/RADAR42522.2020.9114644