NDLI: Detecting dim point target in image data using adaptive prediction filter

Content Provider	IEEE Xplore Digital Library
Author	Yun Hu Guan Hua Zhen-Kang Shen Zhong-Kang Sun
Copyright Year	1995
Description	Author affiliation: Dept. of Electron. Eng., Nat. Univ. of Defense Technol., Hunan, China (Yun Hu; Guan Hua; Zhen-Kang Shen; Zhong-Kang Sun)
Abstract	This paper studies the performance of least mean square (LMS) adaptive filters as prewhitening filters for the detection of point target in image data. The object of interest is assumed to be pixel-size and is obscured by correlated noise of much larger spatial extent. The correlation noise is predicted and subtracted from input signal, leaving components of the point target in the residual output. The noise is suppressed and the target is enhanced relatively. The prewhitened image is then processed by a proper threshold to pick out the candidate target. Experimental results show that such a detector has better operating characteristics than a conventional matched filter in the presence of correlated clutter and noise. For very low SNP, LMS-based detection systems show a considerable reduction in the number of false alarm. Simulation results have been provided at the end of the paper.
Starting Page	197
Ending Page	200
File Size	325933
Page Count	4
File Format	PDF
ISBN	0780326660
ISSN	05473578
DOI	10.1109/NAECON.1995.521937
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1995-05-22
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Adaptive filters Least squares approximation Detectors Matched filters
Content Type	Text
Resource Type	Article

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