A Modulation Recognition Device and Method Combining Higher-Order Statistics and Spectral Peak Features

Abstract

The invention discloses a modulation and recognition device and a method for union high-order statistic and spectral peak features. The modulation and recognition device comprises a signal preprocessing module, a high-order statistic feature extraction module, a spectral peak feature extraction module and a union recognition module. Signals to be recognized and modulated are input into the signal preprocessing module so that filtering, carrier frequency estimating and normalization processing can be carried out to obtain the preprocessing signals, and then the preprocessing signals are input into the high-order statistic feature extraction module and the spectral peak feature extraction module so that the features can be extracted, wherein the features include high-order moment features, high-order accumulation features, constellation cluster point values, feature power spectrum variance features, first-order differential amplitude histogram spectral peak number features and the like. The extracted feature information is input into the union recognition module. A classifier based on the union features carries out mode feature matching on the input signals and outputs recognition results. According to the device and the method, recognition of the SC-FDMA modulation mode is achieved for the first time, the recognition rate of the high-order QAM modulation mode is improved, the judgment threshold of recognition in MFSK classes is expanded, and the device and the method can be applied to the fields of frequency spectrum management, electronic countermeasures and the like.

Description

technical field [0001] The invention belongs to the technical field of communication, and in particular relates to a modulation identification method combining high-order statistics and spectral peak features. Background technique [0002] With the development of modern digital signal processing technology, modulation recognition technology is constantly emerging, which makes the research on communication signal modulation recognition have a very wide range of application value and prospects. Modulation recognition technology is divided into two ways according to its use, civilian and military purposes. For civilian use, the Radio Administration can use modulation identification technology for signal identification and monitoring signal transmission, control information transmission through monitoring, and discover unregistered or illegal transmitters, thereby ensuring the security of the wireless communication environment. In the military, modulation recognition technology...

AI Technical Summary

Problems solved by technology

In 2008, Hsiao-Chun Wu et al. proposed a method of using high-order cumulants under multipath conditions for blind channel estimation and modulation identification. They mainly carried out identification simulations for BPSK, QPSK, 4QAM, 16QAM and 64QAM, and found that It has a performance advantage over existing modulation automatic identification, but this method supports fewer modulation modes for identification

In 2009, Ohara S. et al. proposed an MQAM modulation identification method based on amplitude and cosine, which overcomes the disadvantage that the constellation diagram is greatly affected by noise in the pure amplitude MQAM classification method, and carried out identification simulations for 16QAM and 64QAM

[0006] In terms of related patents, the invention with the application number of 201210150812.5 proposes a communication signal modulation recognition method based on generalized S-exchange, which realizes communication signal recognition by performing generalized S-transform on the input signal by combining short-time Fourier transform and Gaussian window function. But it does not mention the signal identification method for SC-FDMA

The invention with the application number 201210234727.7 proposes a feature extraction and modulation recognition method of communication signals, but it only describes the mechanism of classification recognition, and does not define the modulation recognition of joint high-order statistics and spectral peak features

Images