DocumentCode :
3222923
Title :
A multiple hypotheses testing approach to radar detection and pre-classification
Author :
Greco, Maria ; Gini, Fulvio ; Farina, Alfonso
Author_Institution :
Dipt. di Ingegneria dell´´Informazione, Universita di Pisa, Italy
fYear :
2004
fDate :
26-29 April 2004
Firstpage :
463
Lastpage :
468
Abstract :
This work presents a single-scan-processing approach to the problem of detecting and pre-classifying a radar target that may belong to different target classes. The proposed method is based on a hybrid of the maximum a posteriori (MAP) and Neyman-Pearson (NP) criteria and guarantees the desired constant false alarm rate (CFAR) behavior. The targets are modeled as subspace random signals having zero mean and given covariance matrix. Different target classes are discriminated based on their different signal subspaces, which are specified by their covariance matrices. Performance is investigated by means of numerical analysis and Monte Carlo simulation in terms of probabilities of false alarm, detection and classification. The extra signal-to-noise power ratio necessary to preclassify a target once a detection has occurred is also derived.
Keywords :
Monte Carlo methods; covariance matrices; maximum likelihood detection; military radar; radar detection; radar signal processing; radar target recognition; CFAR; MAP criteria; Monte Carlo simulation; Neyman-Pearson criteria; constant false alarm rate; covariance matrix; maximum a posteriori criteria; multiple hypotheses testing; performance; pre-classification; radar detection; radar target; signal-to-noise power ratio; single-scan-processing; subspace random signals; zero mean; Covariance matrix; Data models; Numerical analysis; Radar detection; Radar theory; Sensor systems; Shape; Surveillance; Target recognition; Testing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Radar Conference, 2004. Proceedings of the IEEE
Print_ISBN :
0-7803-8234-X
Type :
conf
DOI :
10.1109/NRC.2004.1316469
Filename :
1316469
Link To Document :
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