Adapting Sequential Monte-Carlo Estimation to Cooperative Localization in Wireless Sensor Networks

Abstract

Localization is a key function in wireless sensor networks (WSNs). Many applications and internal mechanisms require nodes to know their location. This work proposes a new sequential estimation algorithm for distributed cooperative localization, whose simplicity makes it amenable to self-localization in wireless sensor networks (WSNs), characterized by their restricted resources in energy and computation. The algorithm is inspired in sequential Monte-Carlo estimation techniques, viz. particle filters that excel in robustness and simplicity for estimation applications. However, particle filters require significant amounts of memory and computational power for managing large numbers of particles. The presented technique reduces the number of particles, while retaining the convergence, accuracy and simplicity properties, as demonstrated in simulation experiments