Knowledge-Aided Bayesian Radar Detectors & Their Application to Live Data

This paper considers the problem of adaptive radar detection in Gaussian clutter with unknown spectral properties. We employ a Bayesian approach based on a suitable model for the probability density function (pdf) of the unknown clutter covariance matrix. We devise two detectors based on the general...

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Bibliographic Details
Published in:IEEE transactions on aerospace and electronic systems 2010-01, Vol.46 (1), p.170-183
Main Authors: De Maio, A., Farina, A., Foglia, G.
Format: Article
Language:English
Subjects:
Air traffic control
Bayesian analysis
Bayesian methods
Clutter
Covariance matrix
Detectors
Probability density function
Probability density functions
Radar
Radar applications
Radar clutter
Radar data
Radar detection
Radar signal processing
Signal analysis
Testing
Training
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Summary:This paper considers the problem of adaptive radar detection in Gaussian clutter with unknown spectral properties. We employ a Bayesian approach based on a suitable model for the probability density function (pdf) of the unknown clutter covariance matrix. We devise two detectors based on the generalized likelihood ratio test (GLRT) criterion both one-step and two-step. The suggested decision rules achieve the same performance as the non-Bayesian GLRT detectors when the size of the training set is sufficiently large. However, our new detectors significantly outperform their non-Bayesian counterparts when the training set is small. The analysis is also supported by results on real L-band clutter data from the MIT Lincoln Laboratory phase one radar and on high fidelity radar data from the knowledge-aided sensor signal processing and expert reasoning (KASSPER) program.
ISSN:0018-9251
1557-9603
DOI:10.1109/TAES.2010.5417154