  In solving the non-myopic radar scheduling for multiple smart target tracking
within an active and passive radar network, we need to consider both short-term
enhanced tracking performance and a higher probability of target maneuvering in
the future with active tracking. Acquiring the long-term tracking performance
while scheduling the beam resources of active and passive radars poses a
challenge. To address this challenge, we model this problem as a Markov
decision process consisting of parallel restless bandit processes. Each bandit
process is associated with a smart target, of which the estimation state
evolves according to different discrete dynamic models for different actions -
whether or not the target is being tracked. The discrete state is defined by
the dynamic mode. The problem exhibits the curse of dimensionality, where
optimal solutions are in general intractable. We resort to heuristics through
the famous restless multi-armed bandit techniques. It follows with efficient
scheduling policies based on the indices that are real numbers representing the
marginal rewards of taking different actions. For the inevitable practical case
with unknown transition matrices, we propose a new method that utilizes the
forward Sarsa and backward Q-learning to approximate the indices through
adapting the state-action value functions, or equivalently the Q-functions, and
propose a new policy, namely ISQ, aiming to maximize the long-term tracking
rewards. Numerical results demonstrate that the proposed ISQ policy outperforms
conventional Q-learning-based methods and rapidly converges to the well-known
Whittle index policy with revealed state transition models, which is considered
the benchmark.
