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We track moving targets with a distributed multiple-input multiple-output(MIMO) radar, for which the transmitters and receivers are appropriately pairedand selected with a limited number of radar stations. We aim to maximize thesum of the signal-to-interference-plus-noise ratios (SINRs) of all the targetsby sensibly selecting the transmitter-receiver pairs during the trackingperiod. A key is to model the optimization problem of selecting thetransmitter-receiver pairs by a restless multi-armed bandit (RMAB) model thatis able to formulate the time-varying signals of the transceiver channelswhenever the channels are being probed or not. We regard the estimated meanreward (i.e., SINR) as the state of an arm. If an arm is probed, the estimatedmean reward of the arm is the weighted sum of the observed reward and thepredicted mean reward; otherwise, it is the predicted mean reward. We associatethe predicted mean reward with the estimated mean reward at the previous timeslot and the state of the target, which is estimated via the interactingmultiple model-unscented Kalman filter (IMM-UKF). The optimized selection oftransmitter-receiver pairs at each time is accomplished by using BinaryParticle Swarm Optimization (BPSO) based on indexes of arms, each of which isdesigned by the upper confidence bound (UCB1) algorithm. Above all, amulti-group combinatorial-restless-bandit technique taking into account ofdifferent combinations of transmitters and receivers and the closed-loop schemebetween transmitter-receiver pair selection and target state estimation, namelyMG-CRB-CL, is developed to achieve a near-optimal selection strategy andimprove multi-target tracking performance. Simulation results for differentscenarios are provided to verify the effectiveness and superior performance ofour MG-CRB-CL algorithm.

Combinatorial-restless-bandit-based Transmitter-Receiver Online Selection for Distributed MIMO Radars With Non-Stationary Channels