Matrix analysis
Target Detection in Post-STAP Undernulled Clutter
ASILOMAR '95 Proceedings of the 29th Asilomar Conference on Signals, Systems and Computers (2-Volume Set)
One-and two-stage tunable receivers
IEEE Transactions on Signal Processing
Performance of the adaptive sidelobe blanker detection algorithm inhomogeneous environments
IEEE Transactions on Signal Processing
Rao Test for Adaptive Detection in Gaussian Interference With Unknown Covariance Matrix
IEEE Transactions on Signal Processing - Part II
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Second-order complex random vectors and normal distributions
IEEE Transactions on Signal Processing
A Subspace-Based Adaptive Sidelobe Blanker
IEEE Transactions on Signal Processing
Adaptive beamformer orthogonal rejection test
IEEE Transactions on Signal Processing
Digital Signal Processing
Distributed model-invariant detection of unknown inputs in networked systems
Proceedings of the 2nd ACM international conference on High confidence networked systems
| Hi-index | 35.68 |
In this paper, we address the problem of adaptive detection in homogeneous Gaussian interference, with unknown covariance matrix, after reduction by invariance. Starting from a maximal invariant statistic, which contains all the information for the synthesis of an invariant detector, we devise the Rao test, generalized likelihood ratio test (GLRT), and Durbin test. Moreover, we compare their decision statistics with those of the receivers designed according to the same criteria from the raw data (i.e., before reduction by invariance). We prove that the GLRT in the original data space is statistically equivalent to the GLRT designed after reduction by invariance (under a very mild assumption) and coincide with the conditional uniformly most powerful invariant (C-UMPI) test, obtained conditioning on an ancillary part of the maximal invariant statistic. As to the Rao and Durbin criteria, when they are applied after reduction by invariance, lead to detectors different from the counterparts devised in the raw data domain. At the analysis stage, the performance of the receivers devised in the invariant domain is analyzed in comparison with that of the counterparts synthesized from the original observations.