A block-diagonal growth curve model
Digital Signal Processing
Subspace Selection for Quadratic Detector of Random Signals in Unknown Correlated Clutter
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Narrowband signal detection techniques in shallow ocean by acoustic vector sensor array
Digital Signal Processing
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Subspace-based adaptive detection performance is examined for the generalized likelihood ratio detector based on Wilks' Λ statistic. The problem considered here is detecting the presence of one or more signals of known shape embedded in Gaussian distributed noise with unknown covariance structure. The data is mapped into a subspace prior to detection. The probability of false alarm is independent of the subspace transformation and depends only on subspace dimension. The probability of detection depends on the subspace transformation through a nonadaptive signal-to-noise ratio (SNR) parameter. Subspace processing results in an SNR loss that tends to decrease performance and a gain in statistical stability that tends to increase performance. It is shown that the statistical stability effect dominates the SNR loss for short data records, and subspace detectors can require substantially less SNR than full space detectors for equivalent performance. A method for designing the subspace transformation to minimize the SNR loss is proposed and illustrated through simulations