DocumentCode :
72733
Title :
Parametric space–time detection and range estimation of a small target
Author :
Chengpeng Hao ; Gazor, Saeed ; Orlando, Danilo ; Foglia, Goffredo ; Jun Yang
Author_Institution :
State Key Lab. of Acoust., Inst. of Acoust., Beijing, China
Volume :
9
Issue :
2
fYear :
2015
fDate :
2 2015
Firstpage :
221
Lastpage :
231
Abstract :
In this study, the authors deal with the problem of parametric detection for relatively small targets using space-time adaptive processing (STAP). In contrast to the existing parametric STAP detectors, the proposed detectors perform range estimation by exploiting the spillover of the target energy between consecutive samples. To this end, the authors assume that the received useful signal is known up to a complex unknown deterministic factor parameter and the disturbance signal is modelled as a multichannel autoregressive Gaussian process. Moreover, the authors assume that a set of secondary data is available which are free of signal components, but have the same unknown parameters as the disturbance in the cells under test. Using these assumptions, the so-called simplified generalised likelihood ratio test (GLRT) and the two-step GLRT are derived and assessed. It is worth noting that the simplified GLRT is based on an asymptotic ML estimate of the amplitude, which leads to a simple and closed-form detection statistic. The performance assessment, conducted resorting to both simulated dataset and KASSPER dataset, has shown that the proposed decision schemes can provide accurate estimates of the target position within the cell under test and ensure enhanced detection performance compared with their natural competitors.
Keywords :
object detection; space-time adaptive processing; KASSPER dataset; STAP; closed-form detection statistic; generalised likelihood ratio test; multichannel autoregressive Gaussian process; parametric space-time detection; signal components; space-time adaptive processing; target range estimation;
fLanguage :
English
Journal_Title :
Radar, Sonar & Navigation, IET
Publisher :
iet
ISSN :
1751-8784
Type :
jour
DOI :
10.1049/iet-rsn.2014.0081
Filename :
7046027
Link To Document :
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