Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Handbook of Mathematical Functions, With Formulas, Graphs, and Mathematical Tables,
Handbook of Mathematical Functions, With Formulas, Graphs, and Mathematical Tables,
Structured covariance matrix estimation: a parametric approach
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 05
Estimation of chirp radar signals in compound-Gaussian clutter: acyclostationary approach
IEEE Transactions on Signal Processing
A statistical and physical mechanisms-based interference and noisemodel for array observations
IEEE Transactions on Signal Processing
Analysis of STAP algorithms for cases with mismatched steering andclutter statistics
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Performance of a class of adaptive detection algorithms innonhomogeneous environments
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Exact performance of STAP algorithms with mismatched steering andclutter statistics
IEEE Transactions on Signal Processing
Robust STAP detection in a dense signal airborne radar environment
Signal Processing - Special section: New trends and findings in antenna array processing for radar
Robust adaptive signal processing methods for heterogeneous radar clutter scenarios
Signal Processing - Special section: New trends and findings in antenna array processing for radar
Reiterative robust adaptive thresholding for nonhomogeneity detection in non-gaussian noise
EURASIP Journal on Advances in Signal Processing
Adaptive subspace detection of range-distributed target in compound-Gaussian clutter
Digital Signal Processing
The Empirical Likelihood method applied to covariance matrix estimation
Signal Processing
Knowledge-aided covariance matrix estimation and adaptive detection in compound-Gaussian noise
IEEE Transactions on Signal Processing
Distributed target detection with polarimetric MIMO radar in compound-Gaussian clutter
Digital Signal Processing
Adaptive detection of distributed targets in compound-Gaussian clutter with inverse gamma texture
Digital Signal Processing
Digital Signal Processing
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This work addresses the problem of covariance matrix estimation for adaptive radar detection in correlated heavy tailed clutter. The clutter is modeled as a compound-Gaussian process with unknown statistics. An approximate maximum likelihood (AML) estimator is derived and compared to the maximum likelihood (ML) estimator; their calculation requires the iterative solution of a transcendental equation whose numerical convergence is obtained through the introduction of a constraint in the iteration. The performance of the two "constrained" algorithms is evaluated in terms of the Frobenius norm of the error matrix, of the computational complexity (i.e., the number of iterations), and of the constant false alarm rate (CFAR) property of the adaptive detector which makes use of them. Numerical results show that the AML estimator can be calculated with a very small number of iterations, it has a negligible performance loss with respect to the ML and less computational complexity. Finally, the AML guarantees the desired CFAR property to the detector.