Lightweight Particle Filters Based Localization Algorithm for Mobile Sensor Networks

Nodes localization in mobile sensor networks can be dealt as a problem of mobile object tracking. The particle filters algorithm which is based on Bayesian estimation and Monte Carlo method is an effective tool to deal with these problems. The particle filters algorithm adopts a series of weighted particles to represent the possible position of a mobile object, and then makes use of the present observation to modify the weights of the particles, so as to approach the actual position. The particle filters algorithm can be realized conveniently, however, it has some inherent flaws and suffers from great computation cost and high requirement of storage capacity, thus it couldn ´t be directly applied to the source-restricted mobile sensor networks. In allusion to the characteristic and demand of the mobile sensor networks, an improved and optimized particle filter algorithm was put forward in this paper, named as lightweight particle filters (LPF). This new localization algorithm overcomes the limitations of the particle filters and provides high localization rate and precision. The effectiveness of the LPF is validated through a series of simulations.