GDOP and the CRB for Positioning Systems
Summary :
In this paper, we explore the relationship between Geometric Dilution of Precision (GDOP) and Cramer-Rao Bound (CRB) by tracing back to the original motivations for deriving these two indexes. In addition, the GDOP is served as a sensor-target geometric uncertainty analysis tool whilst the CRB is served as a statistical performance evaluation tool based on the sensor observations originated from target. And CRB is the inverse matrix of Fisher information matrix (FIM). Based on the original derivations for a same positioning application, we interpret their difference in a mathematical view to show that.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E100-A No.2 pp.733-737
- Publication Date
- 2017/02/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.E100.A.733
- Type of Manuscript
- LETTER
- Category
- Information Theory
Authors
Wanchun LI
University of Electronic Science and Technology of China
Ting YUAN
University of Electronic Science and Technology of China
Bin WANG
University of Electronic Science and Technology of China
Qiu TANG
University of Electronic Science and Technology of China
Yingxiang LI
Chengdu University of Information Technology
Hongshu LIAO
University of Electronic Science and Technology of China
Keyword
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Wanchun LI, Ting YUAN, Bin WANG, Qiu TANG, Yingxiang LI, Hongshu LIAO, "GDOP and the CRB for Positioning Systems" in IEICE TRANSACTIONS on Fundamentals,
vol. E100-A, no. 2, pp. 733-737, February 2017, doi: 10.1587/transfun.E100.A.733.
Abstract: In this paper, we explore the relationship between Geometric Dilution of Precision (GDOP) and Cramer-Rao Bound (CRB) by tracing back to the original motivations for deriving these two indexes. In addition, the GDOP is served as a sensor-target geometric uncertainty analysis tool whilst the CRB is served as a statistical performance evaluation tool based on the sensor observations originated from target. And CRB is the inverse matrix of Fisher information matrix (FIM). Based on the original derivations for a same positioning application, we interpret their difference in a mathematical view to show that.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E100.A.733/_p
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@ARTICLE{e100-a_2_733,
author={Wanchun LI, Ting YUAN, Bin WANG, Qiu TANG, Yingxiang LI, Hongshu LIAO, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={GDOP and the CRB for Positioning Systems},
year={2017},
volume={E100-A},
number={2},
pages={733-737},
abstract={In this paper, we explore the relationship between Geometric Dilution of Precision (GDOP) and Cramer-Rao Bound (CRB) by tracing back to the original motivations for deriving these two indexes. In addition, the GDOP is served as a sensor-target geometric uncertainty analysis tool whilst the CRB is served as a statistical performance evaluation tool based on the sensor observations originated from target. And CRB is the inverse matrix of Fisher information matrix (FIM). Based on the original derivations for a same positioning application, we interpret their difference in a mathematical view to show that.},
keywords={},
doi={10.1587/transfun.E100.A.733},
ISSN={1745-1337},
month={February},}
Copy
TY - JOUR
TI - GDOP and the CRB for Positioning Systems
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 733
EP - 737
AU - Wanchun LI
AU - Ting YUAN
AU - Bin WANG
AU - Qiu TANG
AU - Yingxiang LI
AU - Hongshu LIAO
PY - 2017
DO - 10.1587/transfun.E100.A.733
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E100-A
IS - 2
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - February 2017
AB - In this paper, we explore the relationship between Geometric Dilution of Precision (GDOP) and Cramer-Rao Bound (CRB) by tracing back to the original motivations for deriving these two indexes. In addition, the GDOP is served as a sensor-target geometric uncertainty analysis tool whilst the CRB is served as a statistical performance evaluation tool based on the sensor observations originated from target. And CRB is the inverse matrix of Fisher information matrix (FIM). Based on the original derivations for a same positioning application, we interpret their difference in a mathematical view to show that.
ER -
English
