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On Hybrid RSS/TOA Target Localization in NLOS Environments

In this work, target localization problem in adverse indoor environments is addressed, where most (if not all) links are non-line-of-sight (NLOS). Localization accuracy in such environments is highly affected by multipath, which makes the problem very challenging. Hence, in order to enhance the localization accuracy, received signal strength (RSS) and time of arrival (TOA) integrated measurements, are considered here. Nevertheless, the derived joint maximum likelihood (ML) problem is highly non-convex and has no closed-form solution; thus, some approximations are required to solve it. We show that, for small noise power, the ML estimator can be tightly approximated by another (non-convex in general) one, given in a form of a generalized trust region sub-problem (GTRS). Hence, exact solution of the derived estimator can be readily obtained by merely a bisection procedure. The proposed algorithm is compared with the state-of-the-art (SOA) RSS/TOA algorithms, as well as its RSS-only and TOA-only complements. Our simulations validate the effectiveness of the proposed approach, outperforming the SOA algorithms in all considered scenarios, and show the benefit of the measurement fusion.