UWB Systems for Wireless Sensor Networks

Applications of Evolutionary Computation

2015

Abstract. In this paper, we focus on the application of Ultra Wide Band (UWB) technology to the problem of locating static nodes in threedimensional 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.

Section: Scenariomentioning

confidence: 99%

A Swarm Intelligence Approach to $$3$$D Distance-Based Indoor UWB Localization

Applications of Evolutionary Computation

2015

“…The accuracy of the obtained position estimate depends on the errors that affect wireless communications between nodes, which, in indoor environments, are mainly due to non-line-of-sight, multipath, and multiple access interference. To reduce the impact of these sources of errors (thus obtaining a more accurate position estimate), Ultra Wide Band (UWB) signaling is a promising technology, since, on one hand, the large bandwidth allows to penetrate through obstacles and to resolve multipath components and, on the other hand, the high time resolution improves the ranging capability [14].…”

Section: Introductionmentioning

confidence: 99%

Impact of the Number of Beacons in PSO-Based Auto-localization in UWB Networks

Applications of Evolutionary Computation

2013

Abstract. In this paper, we focus on auto-localization of nodes in a static wireless network, under the assumption of known position of a few initial nodes, denoted as "beacons". Assuming that Ultra Wide Band (UWB) signals are used for inter-node communications, we analyze the impact of the number of beacons on the location accuracy. Three different approaches to localization are considered, namely: the Two-Stage Maximum-Likelihood (TSML) method ; the Plane Intersection (PI) method, and Particle Swarming Optimization (PSO). Simulation results show that PSO allows to obtain accurate postion estimates with a small number of beacons, making it an attractive choice to implement effective localization algorithm.

“…Of course, wireless communications are affected by noise, especially in indoor environments, because of non-line-of-sight, multipath and multiple access interference. Ultra Wide Band (UWB) signaling seems a promising technology in this area, since the large bandwidth allows to resolve multipath components and the high time resolution improves their ranging capability (thus making the position estimate more accurate) [2].…”

Section: Introductionmentioning

confidence: 99%

Particle Swarm Optimization for Auto-localization of Nodes in Wireless Sensor Networks

Adaptive and Natural Computing Algorithms

2013

Abstract. In this paper, we consider the problem of auto-localization of the nodes of a static Wireless Sensor Network (WSN) where nodes communicate through Ultra Wide Band (UWB) signaling. In particular, we investigate auto-localization of the nodes assuming to know the position of a few initial nodes, denoted as "beacons". In the considered scenario, we compare the location accuracy obtained with the widely used Two-Stage Maximum-Likelihood algorithm with that achieved with an algorithm based on Particle Swarming Optimization (PSO). Accurate simulation results show that the latter can significantly outperform the former.