UNLOC: Optimal unfolding localization from noisy distance data
Published in Sampling Theory in Signal and Image Processing (STSIP): Special Issue on Harmonic Analysis and Inverse Problems, 2017
Abstract: Target localization is an important problem in signal processing and sensor networks, with many application areas including security (E911, first responders), consumer electronics (location awareness in malls and hospitals), and health monitoring (location-aware patient care). 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 (quadratic objective function with quadratic constraints), 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 labgenerated 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 optimization.