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Compared to the covariance matrix based detector, the proposed model-based adaptive detectors have significantly decreased the training requirements and the computation complexity.
Indeed, the parametric detectors are significantly less dependent on training than the covariance matrix based detector.
Compared to the covariance matrix based detector, all of the proposed adaptive detectors can handle the training-limited case and significantly decreased the computation complexity in compound-Gaussian environment.
Numerical results show that the proposed adaptive CG-PGLRT detectors have dramatically ease the training and computational burden compared to the generalized likelihood ratio test-linear quadratic (GLRT-LQ) which is referred to as covariance matrix based detector and relies more heavily on training.
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Notice that all of the aforementioned STAP detectors proposed in Gaussian and compound-Gaussian environment can be considered as covariance matrix based detectors [8, 14].
Robust counterparts of the correlation matrix based detectors are obtained by substituting a robust correlation matrix estimate as it was discussed in Section 2 (12) mathbf{R}_{r} = left{ begin{array}{ll} mathbf{R},& text{if} mathbf{x} leq eta kappa eta,& text{otherwise} end{array} right.
However, the proposed detector clearly outperforms the signal-model based detector in all cases.
(a) Electrical characterization of tris-4-amidophenyl-thiophene based detector.
Cyclostationary based detector is efficient and more robust than energy detector [9], which is highly susceptible to noise uncertainty.
Measurements of microwave power can be made directly, usually with a thermistor based detector and meter.
Then, several generalized likelihood ratio test (GLRT) based detectors are designed, some of which integrate the prior knowledge of the clutter covariance matrix with the Bayesian approach, while the others are with the heuristic approach.
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