Accession Number : ADA298496
Title : Analysis of the Page Test With Nuisance Parameter Estimation for Various Signal Types.
Descriptive Note : Final rept.,
Corporate Author : NAVAL UNDERSEA WARFARE CENTER NEWPORT DIV NEW LONDON CT NEW LONDON DETACHMENT
Personal Author(s) : Abraham, Douglas A.
PDF Url : ADA298496
Report Date : 22 MAY 1995
Pagination or Media Count : 49
Abstract : The Page test with nuisance parameter estimation is applied to the detection of the following signal types: (a) complex Gaussian distributed data with a shift in mean signal and unknown variance, (b) exponentially distributed data with an unknown noise power, and (c) multivariate complex Gaussian distributed data with a deterministic array signal and an unknown interference covariance matrix. For each signal type, a detector nonlinearity is developed and the Siegmund based approximations to the average sample number derived. Comparison to simulation indicates that the Siegmund based approximations are more accurate than the Wald based approximations and that there is a sensitivity to corruption of the auxiliary data by signal presence when the buffer size is inadequate at low signal to noise ratios. Operating characteristic curves for each of the signal types are generated describing the performance of the detectors as a function of threshold and SNR. The probability of detecting a finite duration signal is approximated by the Brownian motion and moment matching approximation of (Han et al.) and estimated by the Poisson mixture method of (Abraham) where it was observed that the Poisson mixture method provided the least total estimation error. Nonparametric signal onset detection is discussed within the framework of the Page test with nuisance parameter estimation.
Descriptors : *SIGNAL PROCESSING, *NONPARAMETRIC STATISTICS, *POISSON DENSITY FUNCTIONS, BUFFERS, SIMULATION, DISTRIBUTED DATA PROCESSING, DATA TRANSMISSION SYSTEMS, SIZES(DIMENSIONS), PARAMETERS, MATRICES(MATHEMATICS), SIGNAL TO NOISE RATIO, ARRAYS, MOMENTS, POWER SPECTRA, ESTIMATES, NONLINEAR SYSTEMS, APPROXIMATION(MATHEMATICS), ERRORS, INTERFERENCE, COVARIANCE, NOISE, MEAN, DETERMINANTS(MATHEMATICS), BROWNIAN MOTION.
Subject Categories : Cybernetics
Distribution Statement : APPROVED FOR PUBLIC RELEASE