A Low-Complexity Data-Fusion Algorithm Based on Adaptive Weighting for Location Estimation

被引：0
|
作者：
Chiou, Yih-Shyh [1 ]
Tsai, Fuan [1 ]
Yeh, Sheng-Cheng [2 ]
机构：
[1] Natl Cent Univ, Ctr Space & Remote Sensing Res, Tao Yuan 32001, Taiwan
[2] Ming Chuan Univ Taoyuan, Dept Comp & Commun Engn, Taoyuan, 33324, Taiwan
来源：
THIRD INTERNATIONAL CONFERENCE ON INFORMATION SECURITY AND INTELLIGENT CONTROL (ISIC 2012) | 2012年
关键词：
Bayesian approach; data-fusion; error propagation; Kalman filtering; location estimation; tracking;
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：
In this paper, a tracking scheme based on adaptive weighted technique is proposed to reduce the computational load of traditional data-fusion algorithm for heterogeneous measurements. For the location-estimation technique with the data fusion of radio-based ranging measurement and speed-based sensing measurement, the proposed tracking scheme based on the Bayesian approach is handled by a state space model; a weighted technique with the reliability of message passing is based on the error propagation law. As compared with a traditional data-fusion algorithm based on a Kalman filtering approach, the proposed scheme that combines radio ranging measurement with speed sensing measurement for data fusion has much lower computational complexity with acceptable location accuracy.
引用
收藏
页码：294 / 297
页数：4
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