Resource-Constrained Adaptive Search and Tracking for Sparse Dynamic Targets

TL;DR

This paper develops a resource-efficient adaptive sensing strategy for localizing and tracking sparse, dynamic targets over large areas, improving robustness and performance compared to non-adaptive methods.

Contribution

It introduces D-ARAP, a non-myopic adaptive sensing policy that accounts for target motion and amplitude variation, with theoretical bounds and practical advantages.

Findings

D-ARAP outperforms non-adaptive uniform allocation in estimation accuracy.

D-ARAP is more robust to noise and model mismatch.

The policy achieves similar performance to complex dynamic programming methods with lower computational cost.

Abstract

This paper considers the problem of resource-constrained and noise-limited localization and estimation of dynamic targets that are sparsely distributed over a large area. We generalize an existing framework [Bashan et al, 2008] for adaptive allocation of sensing resources to the dynamic case, accounting for time-varying target behavior such as transitions to neighboring cells and varying amplitudes over a potentially long time horizon. The proposed adaptive sensing policy is driven by minimization of a modified version of the previously introduced ARAP objective function, which is a surrogate function for mean squared error within locations containing targets. We provide theoretical upper bounds on the performance of adaptive sensing policies by analyzing solutions with oracle knowledge of target locations, gaining insight into the effect of target motion and amplitude variation as well as sparsity. Exact minimization of the multi-stage objective function is infeasible, but myopic optimization yields a closed-form solution. We propose a simple non-myopic extension, the Dynamic Adaptive Resource Allocation Policy (D-ARAP), that allocates a fraction of resources for exploring all locations rather than solely exploiting the current belief state. Our numerical studies indicate that D-ARAP has the following advantages: (a) it is more robust than the myopic policy to noise, missing data, and model mismatch; (b) it performs comparably to well-known approximate dynamic programming solutions but at significantly lower computational complexity; and (c) it improves greatly upon non-adaptive uniform resource allocation in terms of estimation error and probability of detection.