Adaptive search for dynamic targets under resource constraints

Previous work on resource constrained adaptive search for sparse static targets has produced two-stage allocation policies with desirable properties. For example, for large asymptotic SNR, such policies converge to the true region of interest (ROI) and attain optimal energy allocations relative to exhaustive search. This work investigates the problem of extending previous allocation policies to T ≫ 2 stages, with particular emphasis on cases where the SNR for any particular stage is considerably less than the asymptotic SNR. Furthermore, a new formulation is given that can account for non-static targets, including a dynamic transition model for target location and a population model to account for targets that leave or enter the scene. Under this formulation, a dynamic adaptive resource allocation policy (D-ARAP) is proposed that performs well and has low computational cost. It is shown that this policy provides significant gains over an exhaustive search policy in both static and dynamic target cases with near optimal performance as T → ∞. Moreover, D-ARAP is shown to be more robust than a greedy (myopic) policy when there are outliers or when targets may be obscured for periods of time.