Matrix Factorization-Based Target Localization via Range Measurements With Uncertainty in Transmit Power

In this letter, received signal strength (RSS)- based target localization with uncertainty in transmit power (UTP) is studied. First, the localization-based alternating nonnegative constrained least squares (ANCLS) framework is conducted. A two-phase optimization method, i.e., a matrix factorization-based min-max strategy (MFMM), is then presented to figure out the solution. The first phase of optimization is based on a matrix factorization approach, i.e., active set method (ASM). However, ASM may drop to a local minimum. Therefore, a min-max strategy based on a Taylor linearization approximation is involved in the second phase, where the objective is split into a convex quadratic and a concave term. The target position and UTP are refined simultaneously in the iteration via solving a sequence of convex problems, in which the solution obtained by ASM is to be the initiation. Additionally, to evaluate the effectiveness of MFMM, both the computational complexity and the Cramer-Rao lower bound (CRLB) are analyzed. Simulations are carried out to illustrate the outperformance, compared with other state-of-the-art methods in different scenarios.

I. Introduction

Target localization has been drawn much attention in both military and industrial applications [1], where the data collected by sensors or other facilities are meaningful only when the latter is appropriately geo-referenced [2]. Together with the benefit of cost-effective and synchronization-free among the localization techniques, received signal strength (RSS) has attracted researchers to explore in the literature compared with time of arrival (TOA), time difference of arrival (TDOA), and angle of arrival (AOA) [3]. A considerable body of research has been done concerning target localization using RSS measurements [4]–[12], which drives a notable advance to target localization [13], [14].