QPSK (quadrature phase shift keying) signal and OQPSK (offset quadrature phase shift keying) signal recognition method

Abstract

The invention provides a QPSK (quadrature phase shift keying) signal and OQPSK (offset quadrature phase shift keying) signal recognition method, which comprises the following steps that an instant phase is extracted from I / Q signals subjected to down-conversion; the phase difference of adjacent code elements is calculated on the basis of the instant phase; center normalization instant phase difference is calculated on the basis of the phase difference of the adjacent code elements; the absolute value standard deviation of the center normalization instant phase difference is calculated on the basis of the center normalization instant phase difference; the signal recognition is carried out according to the absolute value standard deviation, signals with the deviation greater than zero are QPSK signals, and signals with the deviation equal to zero are OQPSK signals. The technical scheme has the advantage that the QPSK signals and the OQPSK signals can be simply, conveniently and effectively recognized.

Description

technical field [0001] The invention relates to the field of radio monitoring, in particular to a method for identifying QPSK signals and OQPSK signals. Background technique [0002] In the field of radio monitoring, it is often necessary to identify signals such as illegal radio services and interference sources. Only when the signals are identified can the basis for spectrum management be provided. At present, the existing modulation recognition methods are mainly based on the method of extracting features for recognition. The extracted features mainly include the following types: instantaneous features including amplitude, frequency and phase, statistical features and transform domain features. Among them, statistical feature (such as high-order cumulant) and transform domain (such as wavelet transform) feature engineering calculation is complex and real-time performance is poor. At present, the more effective identification method is the digital modulation signal identi...

AI Technical Summary

Problems solved by technology

Among them, statistical features (such as high-order cumulants) and transform domain (such as wavelet transform) feature engineering calculations are complex and real-time performance is poor

At present, the more effective identification feature method is the digital modulation signal identification algorithm (DMRAs) proposed by British scholars A.K.Nandi and Azzouz, which uses key feature parameters such as instantaneous envelope, phase and frequency as the characteristics of modulation identification. The algorithm calculates The speed is fast, the classification effect is obvious, and it is easier to implement in engineering, but there is no corresponding feature extraction algorithm between some signals, such as the identification between QPSK and OQPSK, the phase characteristics of QPSK and OQPSK are exactly the same, and the spectrum characteristics are also very similar , DMRAs do not give the corresponding feature extraction parameters

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