On detection of the number of signals in presence of white noise
Journal of Multivariate Analysis
A Second-Order Perturbation Expansion for the SVD
SIAM Journal on Matrix Analysis and Applications
Screening among multivariate normal data
Journal of Multivariate Analysis
Covariance matrix estimation for CFAR Detection in correlated heavy tailed clutter
Signal Processing - Signal processing with heavy-tailed models
The probability of a subspace swap in the SVD
IEEE Transactions on Signal Processing
The CFAR adaptive subspace detector is a scale-invariant GLRT
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Knowledge based adaptive processing for ground moving target indication
Digital Signal Processing
CFAR detection for multistatic radar
Signal Processing
Direct data domain STAP using sparse representation of clutter spectrum
Signal Processing
Detection of heterogeneous samples based on loaded generalized inner product method
Digital Signal Processing
Exploiting persymmetry for low-rank Space Time Adaptive Processing
Signal Processing
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This paper addresses the problem of radar target detection in severely heterogeneous clutter environments. Specifically, we present the performance of the normalized matched filter test in a background of disturbance consisting of clutter having a covariance matrix with known structure and unknown scaling plus background white Gaussian noise. It is shown that when the clutter covariance matrix is low rank, the (LRNMF) test retains invariance with respect to the unknown scaling as well as the background noise level and has an approximately constant false alarm rate (CFAR). Performance of the test depends only upon the number of elements, the number of pulses processed in a coherent processing interval, and the rank of the clutter covariance matrix. Analytical expressions for calculating the false alarm and detection probabilities are presented. Performance of the method is shown to degrade with increasing clutter rank especially for low false alarm rates. An adaptive version of the test (LRNAMF) is developed and its performance is studied with simulated data from the KASSPER program. Results pertaining to sample support for subspace estimation, CFAR, and detection performance are presented. Target contamination of training data has a deleterious impact on the performance of the test. Therefore, a technique known as self-censoring reiterative fast maximum likelihood/adaptive power residue (SCRFML/APR) is developed to treat this problem and its performance is discussed. The SCRFML/APR method is used to estimate the unknown covariance matrix in the presence of outliers. This covariance matrix estimate can then be used in the LRNAMF or any other eigen-based adaptive processing technique.