Expectation maximization-based passive localization relying on asynchronous receivers:: Centralized versus distributed implementations

Abstract

This paper considers a passive localization scenario relying on a single transmitter, several receivers and multiple moving targets to be located. The so-called “passive” targets equipped with RFID reflectors are capable of reflecting the signals from the transmitter to the receivers. Existing approaches
assume that the transmitter and receivers are synchronous or quasi-synchronous, which is not always realistic in practical scenarios. Hence, an asynchronous wireless network is considered, where different clock offsets are assumed at different receivers. We propose a centralized expectation maximization-based passive localization method for asynchronous receivers
(EMpLaR) by treating the clock offsets as hidden variables. Thereby, the proposed algorithm makes use of Taylor expansions to arrive at a closed-form maximization. Furthermore, to improve the robustness to link failures and to reduce the energy consumption, we propose a distributed localization approach
based on average consensus formulation to locate the target at each receiver. By applying a quadratic polynomial approximation of the function on which consensus has to be reached, both the computational complexity and the communications overhead are significantly reduced. The Cram ́ r-Rao bound of the target location is derived as a benchmark of our proposed algorithms. Our simulation results show that the proposed centralized and distributed EMpLaR algorithms match the Cram ́ r-Rao bound and significantly improve the localization performance compared to the conventional methods.

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Identifiers

ORCID for Lajos Hanzo: orcid.org/0000-0002-2636-5214

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