Method for effectively recognizing digital modulating signals in non-Gaussian noise

Abstract

The invention discloses a method for effectively recognizing digital modulating signals in non-Gaussian noise. Non-linear transformation is performed on a received signal s(t); the generalized first-order cyclic cumulant and the generalized second-order cyclic cumulant of the received signal s(t) are calculated, and a 2FSK signal is recognized by calculating the characteristic parameters of the received signal s(t) and utilizing a minimum mean square error classifier; the generalized second-order cyclic cumulant of the received signal s(t) is calculated, and by calculating the characteristic parameters of the received signal s(t) and utilizing the minimum mean square error classifier, the number of spectral peaks of a generalized cyclic cumulant magnitude spectrum is detected so that a BPSK signal and an MSK signal can be recognized; the generalized fourth-order cyclic cumulant of the received signal s(t) is calculated, and a QPSK signal, an 8PSK signal and other signals are recognized through the calculated characteristic parameters and the minimum mean square error classifier. The method for effectively recognizing digital modulating signals in non-Gaussian noise solves the problem that signals in Alpha stable distribution noise do not have second or higher order statistics, effectively recognizes the digital modulating signals and can be used for recognizing the modulation mode of the digital modulating signals in the Alpha stable distribution noise.

Description

technical field [0001] The invention belongs to the technical field of communication, in particular to a method for effectively identifying digital modulation signals under non-Gaussian noise. Background technique [0002] Digital signal modulation recognition has very important applications in both military and civilian fields. Traditional digital modulation recognition assumes that the background noise obeys a Gaussian distribution, but in actual wireless communication systems there are often some non-Gaussian distributed noises, which have significant spike-like waveforms and thick probability density function tails. The researchers found that a more effective noise model to describe such non-Gaussian random signals is the Alpha-stable distribution model. Therefore, it is of great practical engineering significance to study the digital signal modulation recognition method under the background of Alpha stable distributed noise. [0003] In recent years, scholars have don...

AI Technical Summary

Problems solved by technology

[0004] The present invention provides a method for effectively identifying digitally modulated signals under non-Gaussian noise, aiming to solve the problem that existing methods cannot simply and effectively identify digitally modulated signals in non-Gaussian noise environments and have poor universality

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