  A smart target, also referred to as a reactive target, can take maneuvering
motions to hinder radar tracking. We address beam scheduling for tracking
multiple smart targets in phased array radar networks. We aim to mitigate the
performance degradation in previous myopic tracking methods and enhance the
system performance, which is measured by a discounted cost objective related to
the tracking error covariance (TEC) of the targets. The scheduling problem is
formulated as a restless multi-armed bandit problem (RMABP) with state
variables, following the Markov decision process. In particular, the problem
consists of parallel bandit processes. Each bandit process is associated with a
target and evolves with different transition rules for different actions, i.e.,
either the target is tracked or not. We propose a non-myopic, scalable policy
based on Whittle indices for selecting the targets to be tracked at each time.
The proposed policy has a linear computational complexity in the number of
targets and the truncated time horizon in the index computation, and is hence
applicable to large networks with a realistic number of targets. We present
numerical evidence that the model satisfies sufficient conditions for
indexability (existence of the Whittle index) based upon partial conservation
laws, and, through extensive simulations, we validate the effectiveness of the
proposed policy in different scenarios.
