A new method of modulation classification for digitally modulated signals

Abstract

A new signal processing method of modulation classification for digitally modulated signals is presented. This method utilizes a signal representation known as the modulation model. The modulation model provides a signal representation that is convenient for subsequent analysis, such as estimating modulation parameters. The modulation parameters to be estimated are the carrier frequency, modulation type, and bit rate. The modulation model is formed via autoregressive spectrum modeling. The modulation model uses the instantaneous frequency and bandwidth parameters as obtained from the roots of the autoregressive polynomial. In particular, the bandwidth parameter and den'vative of the instantaneous frequency are shown to provide excellent measures for information type and rate in addition to being noise robust. Computer simulation results are provided for various modulation types and show the new method to perform well for low carrier to noise ratio (CNR) input signals. IntroductionSignal interception plays an important role in military communications. However, due to the increasing density of the frequency spectrum, the problem of signal interception is becoming more difficult. Previous systems have relied on manual identification of signal parameters, which would then be used for signal classification or demodulation. However, due to the increased activity in the frequency spectrum, manual identification is becoming less practical and automated techniques for modulation classification are becoming desired.Signal interception consists of signal detection followed by estimation of modulation parameters and finally demodulation. A signal of interest (SOI) is generally detected by a spectrum receiver. The frequency band of interest is then further processed to estimate modulation parameters. The modulation parameters considered in this paper are the carrier frequency, modulation type, and bit rate. The modulation types that will be considered are continuous wave (CW), phase shift keying (PSK), and frequency shift keying (FSK). Several methods have been suggested for estimating modulation parameters, i.e. by using analytic signal representations [l], statistical moments [2], and zero crossing rates [3]. This paper uses a modulation model as formed by autoregressive spectrum modeling. The new model allows for an efficient, robust method of estimating modulation parameters.This paper provides a new method for modulation classification. The following section provides the problem formulation, which includes a description of the modulation model in addition to its formulation via autoregressive spectrum modeling. The classification algorithm is then described and a flowchart is provided. This section is followed by computer simulation results and the conclusion of this p a per. Problem FormulationThis paper utilizes a signal representation known as the modulation model to aid in estimating modulation parameters. The modulation model represents a multicomponent signal as a sum of single component signals in terms of...

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