Abstract
Target localization is an important problem in signal processing and sensor networks, with many application areas including security, consumer electronics, and health monitoring. In this paper, we formulate target localization as an inverse problem: given the locations of a set of anchors and noisy distance measures to a target, the localization problem is to estimate the unknown location of the target. We propose to solve the localization problem using an unfolding-based optimization. We show that the corresponding stress optimization, despite being a nonlinear problem, yields a global optimum that can be approximated using an efficient iterative algorithm. We term our computational approach as UNLOC, unfolding-based localization, and benchmark its effectiveness on both synthetic data and lab-generated experimental data. The proposed localization technique generally produces accurate target location, and the quality of localization can be further improved by an appropriate choice of weights in the objective function of the optimization problem.
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