DocumentCode
504217
Title
An analysis of 2D/3D data fusion for a sensor resource reduction
Author
Matsuzaki, Takashi ; Ikeda, Mitsuhisa ; Kameda, Hiroshi
Author_Institution
Inf. Technol. R&D Center, Mitsubishi Electr. Corp., Kanagawa, Japan
fYear
2009
fDate
18-21 Aug. 2009
Firstpage
3026
Lastpage
3031
Abstract
In tracking flying objects using 3D sensors which observe range and angle observations such as elevation and azimuth in 3D sensor system, there may not be enough 3D sensor resources such as a number of radar and radar beams. In consequence, it it difficult to achieve desired tracking accuracy due to the lack of 3D sensor coverage, it cannot be done to achieve users´ desired tracking accuracy. Therefore, to apply 2D sensors for 3D sensor system is needed in term of filling some blank of 3D sensor coverage, and moreover it is necessary to minimize the 3D sensor resources at the same time. 2D sensor observes angle observations (elevation and azimuth). In SICE2008, we have already proposed a 2D/3D data fusion method for launched target. However, this method is not confirmed in any condition, for example, situation based on combination of number of sensors, the location of sensors and performance of sensors, etc. In this paper, we carried on a sensitivity analysis of tracking accuracy for various location of 2D sensor platform with different location of 3D sensors, and various sensors, etc. As the result of the computer simulations, we could confirm one or two 3D sensors with 2D sensor system are superior to three 3D sensor system in a view point of tracking accuracy.
Keywords
Kalman filters; sensitivity analysis; sensor fusion; tracking; 2D data fusion; 2D sensor platform; 3D data fusion; 3D sensor system; Kalman filter; computer simulations; flying object tracking; sensitivity analysis; sensor resource reduction; tracking accuracy; Azimuth; Covariance matrix; Information analysis; Information technology; Radar tracking; Sensitivity analysis; Sensor fusion; Sensor systems; Surveillance; Target tracking; Data Fusion; IMM; Kalman Filter; Motion model; Observation Model; Sensor Resource; Tracking;
fLanguage
English
Publisher
ieee
Conference_Titel
ICCAS-SICE, 2009
Conference_Location
Fukuoka
Print_ISBN
978-4-907764-34-0
Electronic_ISBN
978-4-907764-33-3
Type
conf
Filename
5332945
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