A practical indoor TOA ranging error model for localization algorithm

Abstract

This paper presents an practical RSSI based TOA ranging error model (RITEM) for localization algorithm, which can be used to estimate ranging error interval in real time. In RITEM, ranging error is classified into four classes by the RSSI value in TOA ranging process and ranging error of each class always within a certain interval. RITEM is verified by field tests in two typical indoor environments. Then, RITEM is applied into Ranging Error Classification (REC) based TOA localization algorithm to introduce its application methodology. Experiment result indicates that REC algorithm has significantly improved performance in typical indoor environment, comparing with LS, CN-TOAG and Nano localization algorithms.