Performance Analysis for High Dimensional Non-parametric Estimation in Complicated Indoor Localization

摘要

In this paper, we propose an extended recursive Cramer-Rao lower bound (ER-CRLB) method as a fundamental tool to analyze the performance of wireless indoor localization systems. According to the non-parametric estimation method, the Fisher information matrix of the ER-CRLB is divided into two parts: the state matrix and the auxiliary matrix, which builds a general framework to consider all the possible factors that may influence the estimation performance. Based on this idea, ER-CRLB can fully model the estimation process in the complicated indoor environment, e.g., the sequential position state propagation, target-anchor geometry effect, the NLOS identification, and the related prior information, which are demonstrated in the comprehensive simulations.