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- W2166082412 abstract "In this paper, the problem of detecting a multichannel signal in the presence of spatially and temporally colored disturbance is considered. By modeling the disturbance as a multichannel auto-regressive (AR) model and treating the spatial covariance matrix as a random matrix, a parametric generalized likelihood ratio test (P-GLRT) is developed based on a Bayesian framework. The resulting P-GLRT, which is denoted as the knowledge-aided P-GLRT (KA-PGLRT), employs a fully Bayesian principle and performs a jointly spatio-subtemporal whitening process. The KA-PGLRT detector is able to utilize some prior knowledge through a colored loading step between the prior spatial covariance matrix and the conventional estimate of the P-GLRT. Simulation results verify that the KA-PGLRT detector yields better detection performance over other parametric detectors." @default.
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- W2166082412 date "2011-05-01" @default.
- W2166082412 modified "2023-09-28" @default.
- W2166082412 title "Bayesian parametric GLRT for knowledge-aided space-time adaptive processing" @default.
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- W2166082412 doi "https://doi.org/10.1109/radar.2011.5960553" @default.
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