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Observable Degree Analysis for Multi-Sensor Fusion System

by 1,†, 2, 2,*,† and 2
College of Computer and Information Engineering, Henan University, Kaifeng 475004, China
Institute of Systems Science and Control Engineering, School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Sensors 2018, 18(12), 4197;
Received: 16 October 2018 / Revised: 7 November 2018 / Accepted: 14 November 2018 / Published: 30 November 2018
(This article belongs to the Collection Multi-Sensor Information Fusion)
Multi-sensor fusion system has many advantages, such as reduce error and improve filtering accuracy. The observability of the system state is an important index to test the convergence accuracy and speed of the designed Kalman filter. In this paper, we evaluate different multi-sensor fusion systems from the perspective of observability. To adjust and optimize the filter performance before filtering, in this paper, we derive the expression form of estimation error covariance of three different fusion methods and discussed both observable degree of fusion center and local filter of fusion step. Based on the ODAEPM, we obtained their discriminant matrix of observable degree and the relationship among different fusion methods is given by mathematical proof. To confirm mathematical conclusion, the simulation analysis is done for multi-sensor CV model. The result demonstrates our theory and verifies the advantage of information fusion system. View Full-Text
Keywords: multi-sensor network; observable degree analysis; information fusion multi-sensor network; observable degree analysis; information fusion
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MDPI and ACS Style

Hu, Z.; Chen, T.; Ge, Q.; Wang, H. Observable Degree Analysis for Multi-Sensor Fusion System. Sensors 2018, 18, 4197.

AMA Style

Hu Z, Chen T, Ge Q, Wang H. Observable Degree Analysis for Multi-Sensor Fusion System. Sensors. 2018; 18(12):4197.

Chicago/Turabian Style

Hu, Zhentao, Tianxiang Chen, Quanbo Ge, and Hebin Wang. 2018. "Observable Degree Analysis for Multi-Sensor Fusion System" Sensors 18, no. 12: 4197.

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