In this paper, we focus on the application of Ultra Wide Band (UWB) technology to the problem of locating static nodes in three-dimensional indoor environments, assuming to know the positions of a few nodes, denoted as “beacons.” The localization algorithms which are considered throughout the paper are based on the Time Of Arrival (TOA) of signals traveling between pairs of nodes. In particular, we propose to apply the Particle Swarm Optimization (PSO) algorithm to solve the localization problem and we compare its performance with that of the Two-Stage Maximum-Likelihood (TSML) algorithm. Simulation results show that the former allows achieving accurate position estimates even in scenarios where, because of ill-conditioning problems associated with the network topology, TSML fails.
A Swarm Intelligence Approach to 3D Distance-Based Indoor UWB Localization / Monica, Stefania; Ferrari, Gianluigi. - 9028:(2015), pp. 91-102. [10.1007/978-3-319-16549-3_8]
A Swarm Intelligence Approach to 3D Distance-Based Indoor UWB Localization
Monica Stefania;
2015
Abstract
In this paper, we focus on the application of Ultra Wide Band (UWB) technology to the problem of locating static nodes in three-dimensional indoor environments, assuming to know the positions of a few nodes, denoted as “beacons.” The localization algorithms which are considered throughout the paper are based on the Time Of Arrival (TOA) of signals traveling between pairs of nodes. In particular, we propose to apply the Particle Swarm Optimization (PSO) algorithm to solve the localization problem and we compare its performance with that of the Two-Stage Maximum-Likelihood (TSML) algorithm. Simulation results show that the former allows achieving accurate position estimates even in scenarios where, because of ill-conditioning problems associated with the network topology, TSML fails.
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