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We consider the problem of detecting a known signal with constant magnitude immersed in noise of unknown variance, when the propagation channel is frequency-flat and randomly time-varying within the observation window. A Basis Expansion Model with random coefficients is used for the channel, and a Generalized Likelihood Ratio approach is adopted in order to cope with deterministic nuisance parameters...
We propose a generalization of the matched subspace filter for the detection of unknown signals in a background of non-Gaussian and nonindependent noise. The generalization is based on a modification of the Rao test by including a linear transformation derived from independent component analysis (ICA). Receiver operating characteristic (ROC) curves computed for simulated examples illustrate the significant...
The parametric adaptive matched filter (PAMF) detector for space-time adaptive processing (STAP) detection is re-examined in this paper. Originally, the PAMF detector was introduced by using a multichannel autoregressive (AR) parametric model for the disturbance signal in STAP detection. While the parametric approach brings in benefits such as significantly reduced training and computational requirements...
This work studies the performance of a data-driven reduced rank scheme for rapid timing acquisition in multiple access communications. Our results show that when only a limited amount of preamble bits are available, instead of the true second-order statistics (SOS), the reduced rank scheme provides reliable timing information. Exploiting the structure of the multiple access interference, the data...
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