Improved differential evolution for RSSD-based localization in Gaussian mixture noise

Abstract

Signal strength-based localization approaches are prevalent in wireless sensor networks due to their low cost and simplicity. However, factors such as human interference and heterogeneous sources make approaches based on Gaussian noise and known transmit power unreliable. To address these issues, a received signal strength difference (RSSD) based approach is proposed to localize a source with unknown transmit power and Gaussian mixture noise. First, an RSSD-based nonconvex maximum likelihood (ML) problem is formulated which does not require an approximation or good initial point. Then, an improved differential evolution (IDE) method is given to obtain a global solution. Opposition-based learning (OL) combined with a chaotic map (CM) is used to obtain a robust population and adaptive mutation (AM) with two subpopulations is employed to balance global exploration and convergence. The corresponding Cramér–Rao lower bound (CRLB) for Gaussian mixture noise is derived for comparison purposes. Numerical results are presented which show that the proposed OLAM-IDE method provides better localization accuracy than state-of-the-art approaches.