Enhancing Positioning Accuracy through Direct Position Estimators Based on Hybrid RSS Data Fusion

Abstract

Abstract-In this paper, localization based on Received Signal Strength (RSS) is investigated assuming a path loss log normal shadowing model. On the one hand, indirect RSS-based estimation schemes are investigated; these schemes are based on two steps of estimation: estimation of ranges from RSS and then estimation of position using weighted least square approximation. We show that the performances of this type of schemes depend on the used estimator in the first step. We suggest that typical median estimator must be replaced by maximum likelihood estimator (mode) to enhance the positioning accuracy. On the other hand, a new direct RSS-based estimation scheme of position is proposed; Monte Carlo simulations show that the new estimator performs better than indirect estimators and can be reliable in future hybrid localization systems.

A Hybrid Positioning Method Based on Hypothesis Testing

“…We consider a true ML estimator relying on global optimization and an ML approximation (ML-WLS) in which a local optimizer is initialized with a weighted least squares solution [7], both introduced in Subsection III-B. …”

“…However, an approximate solution can be obtained by initializing a numerical local optimizer with an initial guess, e.g. a weighted least squares (ML-WLS) approach [7].…”

An Improvement in Maximum Likelihood Location Estimation Algorithm in Sensor Networks

“…Finally, M. Laaraiedh in [9] suggests that typical median estimator must be replaced by maximum likelihood estimator to enhance the positioning accuracy and he is proposed a new direct RSS-based estimation scheme of position.…”

A semidefinite programming approach to hybrid localization using RSSI and TOA

“…Assuming Gaussian models independence between considered LDP measurements, the likelihood functions are given receptively for RSSI and TOA by [6], [7]:…”

“…Then, by computing the gradients of these likelihood functions, we obtain easily the different ML estimators for respectively RSSI and TOA [6], [7].…”