Tracking with Sparse and Correlated Measurements via a Shrinkage-based Particle Filter

This paper presents a shrinkage-based particle filter method for tracking a mobile user in wireless networks. The proposed method estimates the shadowing noise covariance matrix using the shrinkage technique. The particle filter is designed with the estimated covariance matrix to improve the tracking performance. The shrinkage-based particle filter can be applied in a number of applications for navigation, tracking and localization when the available sensor measurements are correlated and sparse. The performance of the shrinkage-based particle filter is compared with the posterior Cramer-Rao lower bound, which is also derived in the paper. The advantages of the proposed shrinkage-based particle filter approach are demonstrated via simulation and experimental results

A Kriging Algorithm for Fingerprinting Positioning with received Signal Strengths

Abstract—Received signal strength (RSS) based location fingerprinting is a powerful wireless positioning technique. It estimates the target location by consulting a preliminary database and searching for the best matched RSS fingerprints. The construction and maintenance of a sufficient fingerprint database could be laborious and problematic. This paper proposes a new approach that utilizes the Kriging spatial interpolation algorithm to build complete fingerprint databases from sparsely collected measurements. The interpolation performance is analyzed over various extents of sparsity and number of measurements. The constructed fingerprint databases are utilized to locate a static target and the localization performances are analyzed. It is shown that the Kriging algorithm can be used to build RSS fingerprint databases of good accuracy based on sparsely collected measurements

A. Kiring, C. Liu, N. Salman, I. Esnaola, L. Mihaylova, A Shrinkage-based Particle Filter for Tracking with Correlated Measurements

This paper studies the problem of tracking with wireless sensor networks (WSNs) using received signal strength (RSS) measurements. The log-normal shadowing associated with RSS measurements from a mobile terminal is correlated both in space and time. We propose a particle filter that exploits the temporal and spatial correlation and estimates the covariance matrix of the measurement noise using the shrinkage technique. Simulation results show that using the estimated covariance matrix in the tracking filter improves considerably the filter performance. It is also demonstrated via simulations that the shrinkage-based particle filter exhibits superior performance to the particle filter without shrinkage when limited measurements are available. Results with high accuracy of tracking using the proposed method are presented