By Topic

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

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
A. De Maio ; University of Naples ; A. Farina ; G. Foglia

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.

Published in:

IEEE Transactions on Aerospace and Electronic Systems  (Volume:46 ,  Issue: 1 )