Presentation	2013-08-23 Multi-sensor Tracking Using a Least Squares Estimator Yoshio KOSUGE,
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Abstract(in English)	In a multiple sensor system, a sampling interval is not constant. Therefore, we propose a recursive processing expression of tracking filter with variable sampling intervals including a negative sampling interval using the weighted least squares approach. Here, we use a constant n-th derivative of a position model in three-dimensional space. Whatever the measurement accuracy and the length of sampling interval may be, we illustrate that tracking accuracy of a multiple sensor system is better than that of a single sensor system when using our proposed method. We also illustrate that our proposed method can be derived from Kalman filter equations when the measurement data can be obtained sequentially.
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Keyword(in English)	multiple sensor system / tracking filter / least squares estimation / out of sequence / variable sampling interval
Paper #	SANE2013-48
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Conference Information
Committee	SANE
Conference Date	2013/8/16(1days)
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Paper Information
Registration To	Space, Aeronautical and Navigational Electronics (SANE)
Language	JPN
Title (in Japanese)	(See Japanese page)
Sub Title (in Japanese)	(See Japanese page)
Title (in English)	Multi-sensor Tracking Using a Least Squares Estimator
Sub Title (in English)
Keyword(1)	multiple sensor system
Keyword(2)	tracking filter
Keyword(3)	least squares estimation
Keyword(4)	out of sequence
Keyword(5)	variable sampling interval
1st Author's Name	Yoshio KOSUGE
1st Author's Affiliation	Graduate School of Engineering, Nagasaki University()
Date	2013-08-23
Paper #	SANE2013-48
Volume (vol)	vol.113
Number (no)	184
Page	pp.pp.-
#Pages	6
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