Accession Number : ADA194623
Title : Automatic Classification of Digitally Modulated Signals.
Descriptive Note : Master's thesis,
Corporate Author : AIR FORCE INST OF TECH WRIGHT-PATTERSON AFB OH SCHOOL OF ENGINEERING
Personal Author(s) : DeSimio, Martin P
PDF Url : ADA194623
Report Date : Dec 1987
Pagination or Media Count : 126
Abstract : This experiment investigates the performance of an adaptive technique for the classification of the following types of digitally modulated signals: binary amplitude shift keying (BASK), binary phase shift keying (BPSK), quaternary phase shift keying (QPSK), and binary frequency shift keying (BFSK). The feature extraction process uses the mean and variance of the signal, and magnitudes and locations of the maxima in the spectrum of the signal, the spectrum of the signal squared, and the spectrum of the signal raised to the fourth power. The process of raising the signal to the second and fourth power and searching for narrowband energy near twice and four times the intermediate frequency is shown to provide useful information for the classification of BPSK and QPSK signals. A computer simulation is performed to measure the properties of the classifier. First, the classifier is trained with a set of feature vectors calculated from 20 dB SNR signals. The Least Mean Squares (IMS) algorithm is the adaptive procedure used to generate the weight vectors used to form the linear decision functions.
Descriptors : *ADAPTIVE SYSTEMS, *CLASSIFICATION, *COMPUTERIZED SIMULATION, *EXTRACTION, *MODULATION, *SIGNALS, ALGORITHMS, AUTOMATION, DECISION MAKING, ENERGY, FUNCTIONS, INTERMEDIATE FREQUENCIES, LEAST SQUARES METHOD, LINEARITY, MEAN, NARROWBAND, SPECTRA, VARIATIONS
Subject Categories : Cybernetics
Distribution Statement : APPROVED FOR PUBLIC RELEASE