%0 Journal Article
%T A Comparison of Parametric and Sample-Based Message Representation in Cooperative Localization
%A Jaime Lien
%A Ulric J. Ferner
%A Warakorn Srichavengsup
%A Henk Wymeersch
%A Moe Z. Win
%J International Journal of Navigation and Observation
%D 2012
%I Hindawi Publishing Corporation
%R 10.1155/2012/281592
%X Location awareness is a key enabling feature and fundamental challenge in present and future wireless networks. Most existing localization methods rely on existing infrastructure and thus lack the flexibility and robustness necessary for large ad hoc networks. In this paper, we build upon SPAWN (sum-product algorithm over a wireless network), which determines node locations through iterative message passing, but does so at a high computational cost. We compare different message representations for SPAWN in terms of performance and complexity and investigate several types of cooperation based on censoring. Our results, based on experimental data with ultra-wideband (UWB) nodes, indicate that parametric message representation combined with simple censoring can give excellent performance at relatively low complexity. 1. Introduction Location awareness has the potential to revolutionize a diverse array of present and future technologies. Accurate knowledge of a user's location is essential for a wide variety of commercial, military, and social applications, including next-generation cellular services [1, 2], sensor networks [3, 4], search-and-rescue [5, 6], military target tracking [7, 8], health care monitoring [9, 10], robotics [11, 12], data routing [13, 14], and logistics [15, 16]. Typically, only a small fraction of the nodes in the network, known as anchors, have prior knowledge about their location. The remaining nodes, known as agents, must determine their locations through a process of localization or positioning. The ad hoc and often dynamic nature of wireless networks requires distributed and autonomous localization methods. Moreover, location-aware wireless networks are frequently deployed in unknown environments and hence can rely only on minimal (if any) infrastructure, human maintenance, and a priori location information. Cooperation is an emerging paradigm for localization in which agents take advantage of network connections and interagent measurements to improve their location estimates. Non-Bayesian cooperative localization in wireless sensor networks is discussed in [17]. Different variations of Bayesian cooperation have been considered, including Monte-Carlo sequential estimation [18] and nonparametric belief propagation in static networks [19]. For a comprehensive overview of Bayesian and non-Bayesian cooperative localization in wireless networks, we refer the reader to [20], which also introduces a distributed cooperative algorithm for large-scale mobile networks called SPAWN (sum-product algorithm over a wireless network). This
%U http://www.hindawi.com/journals/ijno/2012/281592/