A Multiindex Policy for Joint Selection of Subarrays and Operational Mode of Distributed Coherent Aperture Radars

The joint selection of subarrays and operational mode plays a crucial role in distributed coherent aperture radar with multiple subarrays, which has received limited attention despite its significance in multitarget tracking. We model the joint selection problem of subarrays and operational mode as a restless multiarmed bandit (RMAB) process, aiming to minimize the expected total discounted error covariance trace value over an infinite time horizon. This article generalizes the conventional binary-action RMAB to the more complex multiaction RMAB (MA-RMAB) process with multiconstraints. The Whittle relaxation with two distinct Lagrange multipliers is utilized to relax the constraints on subarrays and computing resources over an infinite time horizon. A multiindex policy is proposed as a computable suboptimal heuristic for the MA-RMAB model, where the multiindexes are calculated by using the optimal value of the Lagrangian dual problem. The effectiveness of the proposed multiindex policy is validated through numerical simulation.