Drss-Based Localisation Using Weighted Instrumental Variables and Selective Power Measurement

Abstract

Differential received signal strength (DRSS) provides a practical means of localisation for wireless sensor networks. Closed-form location estimators based on a linearised propagation path loss model are computationally efficient and hence suitable for wireless sensor nodes. However, most existing solutions suffer from a significant bias arising from the injection of noise into the measurement data matrix caused by the linearisation process. The instrumental variable (IV) method can reduce the bias by replacing the data matrix with a matrix that is weakly correlated with the noise and strongly correlated with the data matrix — the latter correlation however can be weakened by large noise. This paper addresses the bias problem when the noise is large by using the method of selective power measurement to maintain correlation between the instrumental variable and data matrix. Simulation results show the proposed solution out-performs other existing solutions in terms of the root mean square error (RMSE) and bias over a wide range of noise levels.