Sensor Selection via Maximizing Hybrid Bayesian Fisher Information and Mutual Information in Unreliable Sensor Networks

Abstract

The sensor selection problem is addressed for unreliable sensor networks. The Bayesian Fisher information (BFI) matrix, mutual information (MI) and their relationship are investigated under Gaussian mixture noise conditions. To overcome the flaw that the sensor selection methods based on either BFI matrix or MI could not provide coincident results, the multiple objective optimal (MOP) -based sensor selection approach is developed via minimizing the number of selected sensors while maximizing corresponding BFI matrix and MI. The variable weight decision making (VWDM) and technique for order of preference by similarity to ideal solution (TOPSIS) approaches are then proposed to find the candidate that can better trade off the cost and two performance metrics. Comparison results demonstrated that the proposed method can find a more informative sensor group, and ultimately, its overall localization performance outperforms the sensor selection methods based on BFI or MI.