Adaptive search for sparse dynamic targets

We consider the problem of energy constrained and noise-limited search for targets that are sparsely distributed over a large area. We propose a multiple-stage search algorithm that accounts for complex time-varying target behavior such as transitions among neighboring cells and varying target amplitudes. This work extends the adaptive resource allocation policy (ARAP) introduced in [Bashan et al, 2008] to policies with T ≫ 2 stages. The proposed search strategy is driven by minimization of a surrogate function for energy constrained mean-squared error within locations containing targets. Exact optimization of the multi-stage objective function is infeasible, but myopic optimization yields a closed-form solution. We extend the myopic solution with non-myopic considerations that save a percentage of resources for exploring the scene at large. Empirical evidence suggests that the non-myopic policy performs significantly better than the myopic solution in terms of estimation error, probability of detection, and robustness to model mismatch. Moreover, the provided search policy has low computational complexity compared to state-of-the-art dynamic programming solutions.