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- W2738804975 abstract "This paper deals with the problem of detecting range-spread target in Gaussian disturbance with unknown covariance matrix. A model-based Rao detector is derived by modeling the disturbance as an autoregressive (AR) process with unknown parameters. Meanwhile, the asymptotic expressions for the probabilities of false alarm and detection are derived in closed form, which show that the newly proposed detector is asymptotically constant false alarm rate with respect to the disturbance covariance matrix. The performance assessment conducted by resorting to the simulated data, also in comparison to the previously proposed detectors, has confirmed the effectiveness of the newly proposed detectors." @default.
- W2738804975 created "2017-07-31" @default.
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- W2738804975 date "2017-10-01" @default.
- W2738804975 modified "2023-10-18" @default.
- W2738804975 title "Model-based Rao test for adaptive range-spread target detection" @default.
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- W2738804975 doi "https://doi.org/10.1016/j.dsp.2017.07.011" @default.
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