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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.

Location-aware technologies will revolutionize many aspects of commercial, public service, and military sectors, and are expected to spawn numerous unforeseen applications. A new era of highly accurate ubiquitous location-awareness is on the horizon, enabled by a paradigm of cooperation between nodes. In this paper, we give an overview of cooperative localization approaches and apply them to ultrawide bandwidth (UWB) wireless networks. UWB transmission technology is particularly attractive for short- to medium-range localization, especially in GPS-denied environments: wide transmission bandwidths enable robust communication in dense multipath scenarios, and the ability to resolve subnanosecond delays results in centimeter-level distance resolution. We will describe several cooperative localization algorithms and quantify their performance, based on realistic UWB ranging models developed through an extensive measurement campaign using FCC-compliant UWB radios. We will also present a powerful localization algorithm by mapping a graphical model for statistical inference onto the network topology, which results in a net-factor graph, and by developing a suitable net-message passing schedule. The resulting algorithm (SPAWN) is fully distributed, can cope with a wide variety of scenarios, and requires little communication overhead to achieve accurate and robust localization., Belgian American Educational Foundation, inc., JPL Strategic University Research Partnerships, National Science Foundation (U.S.) (Grant ECCS-0636519), National Science Foundation (U.S.) (Grant ANI-0335256)

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007., Includes bibliographical references (p. 89-93)., Location-aware technologies have the potential to revolutionize computing, cellular services, sensor networks, and many other commercial, military, and social applications. In wireless networks, accurate information about an agent's location can give meaning to observed data and facilitate the agent's interactions with its surroundings and neighbors. Determining the location of one or more agents, known as localization or positioning, is a fundamental challenge. Most existing localization methods rely on existing infrastructure and hence lack the flexibility and robustness necessary for large ad-hoc networks. In this thesis, we describe a framework for localization that overcomes these limitations by utilizing cooperation: the agents in the network work together to determine their individual locations. We derive a practical algorithm for cooperative localization by formulating the problem as a factor graph and applying the sum-product algorithm. Each agent uses relative positioning measurements and probabilistic location information from its neighbors to iteratively update its location estimate. We investigate the performance of this algorithm in a network of ultra-wideband (UWB) nodes, which are well-suited for localization due to their potential to measure inter-node distances with high accuracy. Realistic models of UWB ranging error, based on an extensive measurement campaign in several indoor environments, are incorporated into the localization algorithm. Using the experimental data and simulations, we quantify the benefits that cooperation brings to localization., by Jaime Lien., M.Eng.