Distributed state estimation and data fusion in wireless sensor networks using multi-level quantized innovation

被引：1
|
作者：
Zhi ZHANG [1 ,2 ]
Jianxun LI [1 ,2 ]
Liu LIU [3 ]
机构：
[1] Department of Automation, Shanghai Jiao Tong University
[2] Key Laboratory of System Control and Information Processing, Ministry of Education of China
[3] College of Telecommunications & Information Engineering, Nanjing University of Posts and Telecommunications
来源：
Science China(Information Sciences) | 2016年 / 59卷 / 02期
基金：
中国国家自然科学基金;
关键词：
data fusion; distributed state estimation; target tracking; Kalman filtering; quantization; wireless sensor networks;
D O I：
暂无
中图分类号：
TN929.5 [移动通信]; TP212.9 [传感器的应用]; TP202 [设计、性能分析与综合];
学科分类号：
080202 ; 080402 ; 080904 ; 0810 ; 081001 ; 0811 ; 081101 ; 081102 ;
摘要：
Low energy consumption and limited power supply are significant factors for wireless sensor networks(WSNs); thus, distributed state estimation and data fusion with quantized innovation are explored. The universal features of practical WSNs are investigated, and a dynamic transmission strategy is introduced. Furthermore,quantization state estimation based on Bayesian theory is derived. Unlike previous algorithms suitable for processing scalar measurement, the proposed distributed data fusion algorithm is applicable to general vector measurement. Furthermore, the efficiency of the proposed dynamic transmission strategy is analyzed. It is concluded that the proposed algorithm is more efficient than previous methods, and its estimation accuracy comparable to that of the standard Kalman filtering, which is based on analog-amplitude vector measurement.
引用
收藏
页码：217 / 231
页数：15
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