Two-Dimensional Multivariate Parametric Models for Radar Applications-Part I: Maximum-Entropy Extensions for Toeplitz-Block Matrices

In a series of two papers, a new class of parametric models for two-dimensional multivariate (matrix-valued, space-time) adaptive processing is introduced. This class is based on the maximum-entropy extension and/or completion of partially specified matrix-valued Hermitian covariance matrices in bot...

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Bibliographic Details
Published in:IEEE transactions on signal processing 2008-11, Vol.56 (11), p.5509-5526
Main Authors: Abramovich, Y.I., Johnson, B.A., Spencer, N.K.
Format: Article
Language:English
Subjects:
Adaptive arrays
Adaptive processing
Airborne radar
Applied sciences
Australia
autoregressive
Clutter
Covariance matrix
Exact sciences and technology
Information, signal and communications theory
Mathematical models
Miscellaneous
Parametric statistics
Phased arrays
Radar applications
Sensor arrays
Signal processing
Spaceborne radar
stationary interference
Studies
Telecommunications and information theory
time-varying
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Summary:In a series of two papers, a new class of parametric models for two-dimensional multivariate (matrix-valued, space-time) adaptive processing is introduced. This class is based on the maximum-entropy extension and/or completion of partially specified matrix-valued Hermitian covariance matrices in both the space and time dimensions. This first paper considers the more restricted class of Toeplitz Hermitian covariance matrices that model stationary clutter. If the clutter is stationary only in time then we deal with a Toeplitz-block matrix, whereas clutter that is stationary in time and space is described by a Toeplitz-block-Toeplitz matrix. We first derive exact expressions for this new class of 2-D models that act as approximations for the unknown true covariance matrix. Second, we propose suboptimal (but computationally simpler) relaxed 2-D time-varying autoregressive models (ldquorelaxationsrdquo) that directly use the non-Toeplitz Hermitian sample covariance matrix. The high efficiency of these parametric models is illustrated by simulation results using true ground-clutter covariance matrices provided by the DARPA KASSPER Dataset 1, which is a trusted phenomenological airborne radar model, and a complementary AFRL dataset.
ISSN:1053-587X
1941-0476
DOI:10.1109/TSP.2008.929868