Convex Combination for Wireless Localization Using Biased RSS Measurements

Abstract

Received signal strength- (RSS-) based localization in wireless sensor networks (WSNs) has attracted significant attention due to its advantages of low cost and simple implementation. In practice, RSS measurements may suffer from sensor biases, which deteriorates the localization accuracy. However, most of the existing localization methods are designed for bias-free measurements. In this paper, we propose a convex combination method for RSS localization in the presence of sensor biases. The parameter vector composed of unknown location and sensor biases is estimated simultaneously by using a convex combination of some virtual points. These virtual points form a convex hull, into which the parameter vector falls with large probability. By this, the original nonconvex estimation problem is converted to be convex. Numerical examples demonstrate the superiority of the proposed method in terms of localization accuracy, compared to the existing semidefinite programming (SDP) methods.