Robust Convex Approximation Methods for TDOA-Based Localization under NLOS Conditions - 2016

PROJECT TITLE:

Robust Convex Approximation Methods for TDOA-Based Localization under NLOS Conditions - 2016

ABSTRACT:

In this paper, we tend to develop a unique sturdy optimization approach to source localization using time-distinction-of-arrival (TDOA) measurements that are collected under non-line-of-sight (NLOS) conditions. A key feature of our approach is that it will not need data of the distribution or statistics of the NLOS errors, that are usually tough to obtain in apply. Instead, it only assumes that the NLOS errors have bounded supports. Based on this assumption, we tend to formulate the TDOA-primarily based supply localization downside as a sturdy least squares (RLS) drawback, in which a location estimate that is sturdy against the NLOS errors is sought. Since the RLS drawback is non-convex, we have a tendency to propose two efficiently implementable convex relaxation-primarily based approximation ways to tackle it. We then conduct a thorough theoretical analysis of the approximation quality and computational complexity of these 2 strategies. In particular, we have a tendency to establish conditions underneath which they will yield a unique localization of the source. Simulation results on both synthetic and real information show that the performance of our approach underneath varied NLOS settings is very stable and is considerably better than that of many existing non-strong approaches.

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