Two-Dimensional Multivariate Parametric Models for Radar Applications-Part II: Maximum-Entropy Extensions for Hermitian-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.5527-5539
Main Authors: Abramovich, Y.I., Johnson, B.A., Spencer, N.K.
Format: Article
Language:English
Subjects:
Adaptive filters
Adaptive processing
Applied sciences
autoregressive
Clutter
Covariance matrix
Degradation
Exact sciences and technology
Information, signal and communications theory
Mathematical models
Maximum likelihood estimation
Miscellaneous
nonstationary interference
Parametric statistics
Process design
Radar applications
Signal processing
Signal to noise ratio
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. The first paper considered the more restricted class of Hermitian Toeplitz-block covariance matrices that model stationary clutter. This second paper deals with the more general class of Hermitian-block covariance matrices that model nonstationary clutter. For our recently proposed 2-D time-varying autoregressive (TVAR) model, we derive optimal and computationally practical suboptimal methods for calculating such parametric models. The maximum-likelihood covariance matrix estimate for the 2-D TVAR model is also derived. The efficacy of the introduced models is illustrated by signal-to-interference-plus-noise ratio (SINR) degradation results obtained when applying the covariance matrix models to space-time adaptive processing filter design, compared with the true clutter covariance matrix provided by the DARPA KASSPER dataset.
ISSN:1053-587X
1941-0476
DOI:10.1109/TSP.2008.929867