A Robust Algorithm for Classification and Rejection of NLOS Signals in Narrowband Ultrasonic Localization Systems

This paper develops a novel algorithm for classifying and rejecting non-line-of-sight ultrasonic signals for use in indoor positioning and measurement systems. The algorithm consists of two parts, an initial probability estimate based upon signal amplitude followed by an iteratively reweighed least squares regression that utilizes the probabilities in the weight update step. This method allows for reflections to be identified and rejected while simultaneously calculating the position of a receiving node. The algorithm has been tested on a data collected during experiments designed to generate many challenging specular reflections. The experiments were conducted with transducers in a variety of different positions, and amplitude-modulated ultrasonic signals were used with four different envelopes. The algorithm was capable of correctly classifying over 96% of narrowband signals with severe multipath effect at low computational cost.