Advanced location-tracking systems in home, automotive and public transportation environments

In this paper, a novel distance based source-localization algorithm is proposed for location-tracking applications in home, automotive and public transportation environments. Based on the least squares (LS) formulation of the source-localization problem, we develop an optimization algorithm that proves mostly unaffected by the number of local minima of the LS-objective function. The algorithm, referred to as range global distance continuation (R-GDC), consists of an iterative procedure in which, at each k-th iteration, the original objective is smoothed by a Gaussian kernel (smoothing function) and minimized by a steepest-descent (SD) method. The results reveal that the R-GDC algorithm, not only outperforms the alternative methods, but also achieves a localization error that is close to theoretical position error bound (PEB).