A Study on Particle Filter based on KLD-Resampling for Wireless Patient Tracking

Abstract

In this paper, we consider a typical health care system via the help of Wireless Sensor Network (WSN) for wireless patient tracking. The wireless patient tracking module of this system performs localization out of samples of Received Signal Strength (RSS) variations and tracking through a Particle Filter (PF) for WSN assisted by multiple transmit-power information. We propose a modified PF, Kullback-Leibler Distance (KLD)-resampling PF, to ameliorate the effect of RSS variations by generating a sample set near the high-likelihood region for improving the wireless patient tracking. The key idea of this method is to approximate a discrete distribution with an upper bound error on the KLD for reducing both location error and the number of particles used. To determine this bound error, an optimal algorithm is proposed based on the maximum gap error between the proposal and Sampling Important Resampling (SIR) algorithms. By setting up these values, a number of simulations using the health care system's data sets which contains the real RSSI measurements to evaluate the location error in term of various power levels and density nodes for all methods. Finally, we point out the effect of different power levels vs. different density nodes for the wireless patient tracking.