In this paper, we propose an extended recursive Cramér-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.
CITATION STYLE
Zhao, Y., Fan, X., & Xu, C. Z. (2016). Performance analysis for high dimensional non-parametric estimation in complicated indoor localization. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9798 LNCS, pp. 295–304). Springer Verlag. https://doi.org/10.1007/978-3-319-42836-9_27
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