Posterior Cramer-Rao Lower Bound of Target Tracking System using Quantized Innovations

被引：0

作者：

Liu Zhaolei ^{[1
]}

Zhang Zhi ^{[1
]}

Li Jianxun ^{[2
]}

机构：

[1] Nanjing Res Inst Elect Technol, Nanjing, Peoples R China

[2] Shanghai Jiao Tong Univ, Dept Automat, Shanghai, Peoples R China

来源：

ICCAIS 2019: THE 8TH INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION AND INFORMATION SCIENCES | 2019年

关键词：

Posterior Cramer-Rao lower bound; state estimation; quantization; wireless sensor networks; WIRELESS SENSOR NETWORKS;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper explores the problem of posterior Cramer-Rao lower bound (PCRLB) for the state estimation algorithms with quantized innovations in wireless sensor networks (WSNs). By the unscented Kalman filter (UKF), we derive the theoretical lower bound to evaluate the performances of this kind of estimators. Numerical example is provided in support of theoretical analysis, and the performances of the estimators are benchmarked by the proposed PCRLB.

引用

页数：5

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