TY - JOUR
T1 - Knowledge-aided adaptive coherence estimator in stochastic partially homogeneous environments
AU - Wang, Pu
AU - Sahinoglu, Zafer
AU - Pun, Man On
AU - Li, Hongbin
AU - Himed, Braham
PY - 2011
Y1 - 2011
N2 - This letter introduces a stochastic partially homogeneous model for adaptive signal detection. In this model, the disturbance covariance matrix of training signals, R, is assumed to be a random matrix with some a priori information, while the disturbance covariance matrix of the test signal, R 0, is assumed to be equal to λR, i.e., R0= λR. On one hand, this model extends the stochastic homogeneous model by introducing an unknown power scaling factor λ between the test and training signals. On the other hand, it can be considered as a generalization of the standard partially homogeneous model to the stochastic Bayesian framework, which treats the covariance matrix as a random matrix. According to the stochastic partially homogeneous model, a scale-invariant generalized likelihood ratio test (GLRT) for the adaptive signal detection is developed, which is a knowledge-aided version of the well-known adaptive coherence estimator (ACE). The resulting knowledge-aided ACE (KA-ACE) employs a colored loading step utilizing the a priori knowledge and the sample covariance matrix. Various simulation results and comparison with respect to other detectors confirm the scale-invariance and the effectiveness of the KA-ACE.
AB - This letter introduces a stochastic partially homogeneous model for adaptive signal detection. In this model, the disturbance covariance matrix of training signals, R, is assumed to be a random matrix with some a priori information, while the disturbance covariance matrix of the test signal, R 0, is assumed to be equal to λR, i.e., R0= λR. On one hand, this model extends the stochastic homogeneous model by introducing an unknown power scaling factor λ between the test and training signals. On the other hand, it can be considered as a generalization of the standard partially homogeneous model to the stochastic Bayesian framework, which treats the covariance matrix as a random matrix. According to the stochastic partially homogeneous model, a scale-invariant generalized likelihood ratio test (GLRT) for the adaptive signal detection is developed, which is a knowledge-aided version of the well-known adaptive coherence estimator (ACE). The resulting knowledge-aided ACE (KA-ACE) employs a colored loading step utilizing the a priori knowledge and the sample covariance matrix. Various simulation results and comparison with respect to other detectors confirm the scale-invariance and the effectiveness of the KA-ACE.
KW - Bayesian inference
KW - generalized likelihood ratio test
KW - knowledge-aided
KW - partially homogeneous model
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U2 - 10.1109/LSP.2011.2107510
DO - 10.1109/LSP.2011.2107510
M3 - Article
AN - SCOPUS:79551649524
SN - 1070-9908
VL - 18
SP - 193
EP - 196
JO - IEEE Signal Processing Letters
JF - IEEE Signal Processing Letters
IS - 3
M1 - 5696739
ER -