A RSS Based Indoor Tracking Algorithm via Particle Filter and Probability Distribution

Indoor positioning system that make use of received signal strength and existing wireless local area network infrastructure have recently been the focus for supporting location-based services. This paper presents a new indoor location tracking algorithm, that use:(1)signal strength probability distribution estimated by histogram method, addressing the noisy wireless channel, and (2)particle filter to deal with the nonlinear system model, which can approximate the optimal Bayesian estimate. Numerical simulation shows the new algorithm outperforms the tracking algorithm using Kalman filter in the former research.