RBGF: Recursively Bounded Grid-Based Filter for Indoor Position Tracking Using Wireless Networks

Abstract

Numerical methods for recursive Bayesian estimation are widespread in position tracking of robotics. However, challenges arise to indoor network positioning due to the limited processing power and inaccurate ranging measurements of low-end network nodes. For efficient and robust indoor position tracking, we incorporate a recursive bound to a grid-based filter namely RBGF, which approximates the posterior of the target's position by a grid of weighted cells over a bounded state-space. The state-space (the set in which the state samples can take) is recursively confined based on both the previous estimation and current measurements, therefore, the grid cells converge to the true state and the effect of non-line-of-sight (NLOS) measurements is bounded. Experimental results by an indoor sensor test-bed demonstrate RBGF achieves the average and the worst-case of positioning errors about 1 meter and 3 meters, respectively on condition that the average ranging error is about 3 meters.