NDLI: Performance analysis of bootstrap based distributed detector under correlated K-distributed clutter

Content Provider	IEEE Xplore Digital Library
Author	Xiangyang Liu Wei Jin Jia Xu Jin Pan Yingning Peng
Copyright Year	2009
Description	Author affiliation: Department of Electronic Engineering, Tsinghua University, Beijing, China, 100084 (Jia Xu; Yingning Peng) \|\| First Department, Xi'an Communication College, Xi'an, China, 710106 (Xiangyang Liu; Wei Jin; Jin Pan)
Abstract	To deal with the signal detection with multiple sensors under correlated K-distributed clutter with unknown parameter, a bootstrap based distributed detector (BBDD for short) was presented where detection decisions of local sensors are transmitted from each bootstrap detector to the data fusion center, where the overall decision is made based on k-out-of-N fusion rule. Considering the assumption that the samples from different range cells are independent, block is extracted along the direction of azimuth. Monte Carlo simulations show that the BBDD has much higher CFAR capability than the distributed generalized sign detector (DGSD for short) with the same fusion rule proposed in the literature. However, under independent k-distributed clutter, BBDD has a signal-to-clutter loss of up to 3 dB compared with DGSD. The BBDD, therefore, is a good choice for signal detection under correlated K-distributed clutter.
Starting Page	556
Ending Page	559
File Size	230392
Page Count	4
File Format	PDF
ISBN	9781424427314
DOI	10.1109/APSAR.2009.5374225
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-10-26
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Space vector pulse width modulation data fusion bootstrap detector Sensor fusion Radar applications distributed detection Azimuth Radar clutter Radar detection Performance analysis Envelope detectors Signal detection dependent k-distributed clutter Testing
Content Type	Text
Resource Type	Article

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