Journal ArticleDOI
Optimal linear-quadratic systems for detection and estimation
B. Picinbono,P. Devaut +1 more
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It is shown here that the Gaussian assumption can be removed, and a complete solution is presented for an arbitrary probability distribution with finite fourth-order moments.Abstract:
The problem of linear-quadratic systems for detection has long been solved by assuming the deflection criterion and Gaussian noise. It is shown here that the Gaussian assumption can be removed, and a complete solution is presented for an arbitrary probability distribution with finite fourth-order moments. The optimal solution can always be obtained by solving a linear system of equations. Some properties of the optimal systems are developed for particular examples of nonGaussian noise. It is shown that there is a strong relationship between linear-quadratic optimal detection and optimal estimation, which extends results known for the purely linear case. >read more
Citations
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Cooperative Sensing for Primary Detection in Cognitive Radio
TL;DR: This work designs a linear-quadratic (LQ) fusion strategy based on a deflection criterion for this problem, which takes into account the correlation between the nodes and shows that when the observations at the sensors are correlated, the LQ detector significantly outperforms the counting rule.
Journal ArticleDOI
Signal interception: performance advantages of cyclic-feature detectors
TL;DR: The spectral-line regenerators can outperform both types of radiometers by a wide margin and are quantified in terms of receiver operating characteristics for several noise and interference environments and receiver collection times.
Journal ArticleDOI
A bibliography on nonlinear system identification
TL;DR: The present bibliography represents a comprehensive list of references on nonlinear system identification and its applications in signal processing, communications, and biomedical engineering.
Journal ArticleDOI
Sub-optimum coherent radar detection in a mixture of K-distributed and Gaussian clutter
TL;DR: In this article, two suboptimum procedures for coherent detection of a radar target signal, in the presence of a mixture of K-distributed and Gaussian distributed clutter, are presented.
Journal ArticleDOI
Adaptive Principal component EXtraction (APEX) and applications
TL;DR: A neural network model (APEX) for multiple principal component extraction that is applicable to the constrained PCA problem where the signal variance is maximized under external orthogonality constraints and the exponential convergence of the network is formally proved.
References
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Journal ArticleDOI
Detectors for discrete-time signals in non-Gaussian noise
James H. Miller,John B. Thomas +1 more
TL;DR: The structure and performance of a class of nonlinear detectors for discrete-time signals in additive white noise are investigated and three general classes of symmetric, unimodal, univariate probability density functions are introduced that are generalizations of the Gaussian, Cauchy, and beta distributions.
Journal ArticleDOI
Robust detection of a known signal in nearly Gaussian noise
R. Martin,S. Schwartz +1 more
TL;DR: A detector that is not nonparametric, but that nevertheless performs well over a broad class of noise distributions is termed a robust detector.
Journal ArticleDOI
Detection in the presence of spherically symmetric random vectors
TL;DR: A theorem characterizing the form of SS random vectors X is proved and the problem of detecting a known signal vector in the presence of X + N when \rho =I is looked at.
Journal ArticleDOI
Detection of weak signals in non-Gaussian noise
Ning Hsing Lu,B. Eisenstein +1 more
TL;DR: The asymptotic detection performance of the locally optimum detector under non-Gaussian conditions is derived and compared with that for the corresponding detector optimized for operations in Gaussian noise.
Nonparametric detection
TL;DR: In this article, the authors consider some of the simpler nonparametric detection schemes and compare their asymptotic relative efficiencies to those of detectors which are optimal in the Neyman-Pearson sense.