Robust Estimators for Variance-Based Device-Free Localization and Tracking

Device-free localization systems, such as variance-based radio tomographic imaging (VRTI), use received signal strength (RSS) variations caused by human motion in a static wireless network to locate and track people in the area of the network, even through walls. However, intrinsic motion, such as branches moving in the wind or rotating or vibrating machinery, also causes RSS variations which degrade the performance of a localization system. In this paper, we propose a new estimator, least squares variance-based radio tomography (LSVRT), which reduces the impact of the variations caused by intrinsic motion. We compare the novel method to subspace variance-based radio tomography (SubVRT) and VRTI. SubVRT also reduces intrinsic noise compared to VRTI, but LSVRT achieves better localization accuracy and does not require manually tuning additional parameters compared to VRTI. We also propose and test an online calibration method so that LSVRT and SubVRT do not require “empty-area” calibration and thus can be used in emergency situations. Experimental results from five data sets collected during three experimental deployments show that both estimators, using online calibration, can reduce localization root mean squared error by more than 40 percent compared to VRTI. In addition, the Kalman filter tracking results from both estimators have 97th percentile error of 1.3 m, a 60 percent reduction compared to VRTI.