Abstract: We consider the problem of energy constrained and noise-limited search for targets that are sparsely distributed over a large area. We propose a multi-scale search algorithm that significantly reduces the search time of the adaptive resource allocation policy (ARAP) introduced in [Bashan et all, 2008]. Similarly to ARAP, the proposed approach scans a Q-cell partition of the search area in two stages: first the entire domain is scanned and second a subset of the domain, suspected of containing targets, is re-scanned. The search strategy of the proposed algorithm is driven by maximization of a modified version of the previously introduced ARAP objective function, which is a surrogate for energy constrained target detection performance. We analyze the performance of the proposed multistage ARAP approach and show that it can reduce mean search time with respect to ARAP for equivalent energy constrained detection performance. To illustrate the potential gains of MARAP, we simulate a moving target indicator (MTI) radar system and show that M-ARAP achieves an estimation performance gain of 7 dB and a 85% reduction in scan time as compared to an exhaustive search. This comes within 1 dB of the previously introduced ARAP algorithm at a fraction of its required scan time.
Impact: This work provides a policy for mulit-scale search for sparse targets under total resource constraints, such as available scan time and/or computational resources. The proposed policy enhance previous work by considering the case where the number of measurements are constrained as well.
Journal: IEEE Transactions on Signal Processing, May 2011. DOI: 10.1109/TSP.2011.2112353. Full text available here.
Submitted by Gregory Newstadt, Graduate Student Research Assistant, Electrical Engineering and Computer Science, College of Engineering. firstname.lastname@example.org