Research On Parameter Estimation And Modulation Identification Of Communication Signals

With the development of communication technology,the types of modulation signals that can be transmitted in digital communication system are becoming more and more complicated,and the communication environment is becoming more and more bad.Therefore,it is of great value to study the automatic identification technology of digital modulation in non-cooperative communication environment.The modulation identification technology of digital communication signal mainly includes two parts:parameter estimation and classification identification of modulation mode.The part of parameter estimation can recover the modulated signal to baseband signal so as to facilitate subsequent feature parameter extraction and signal demodulation.Digital modulation recognition technology is widely used in civil and military fields,such as radio spectrum monitoring and electronic countermeasures.In the field of modulation recognition of communication signals,the research in bad channel environment has become hotspots,such as low signal-to-noise ratio(SNR),non-Gaussian noise and co-channel multi-signals environment.Moreover,this research field is mostly around the cyclic cumulants,wavelet transform,high-order cumulants,neural network classifier and other technologies.Considering the complexity of the algorithm and the effect of recognition,this thesis also studys modulation recognition on the basis of the above theory.Two parts are studied in this thesis,include parameter estimation and statistical recognition.The contents are as follows:Firstly,several classical parameter estimation methods are studied and simulated,including carrier frequency estimation and symbol rate estimation.The carrier frequency estimation studies zero-crossing detection method,frequency domain centering method and phase difference division method,and compares the three methods.Then,in order to accurately estimate the carrier frequency of MPSK and MQAM signals at low SNR,the algorithm combining cyclic overlapping power spectrum and fourth-order cyclic cumulants is studied,and the simulation results show that the error rate is less than 0.5 % when the SNR is 3 dB or more.And the symbol rate estimation studies the zero-crossing detection method and the frequency domain center method,and compares the results.In addition,the double wavelet transform method is analyzed and simulated.And the algorithm of the symbol rate estimation of MPSKsignal based on wavelet transform is optimized without prior information.Results show that the accuracy rate of estimation of the algorithm is improved.In this algorithm,five steps are carried out,includes calculating cyclic overlapping power spectrum,wavelet denoising,extraction of instantaneous phase baseband sequence,wavelet transform and calculation of power spectrum.The accuracy rate of the algorithm is higher than 90 %when the SNR is 2 dB or more.Secondly,the recognition algorithm of digital modulated signals based on high-order cumulants,wavelet transform and neural network classifier is studied.Firstly,the algorithm constructs a characteristic parameter which can be used to classify signals according to its theory value of high-order cumulants,and then constructs two characteristic parameters based on the analysis of the amplitude and frequency information of the signal after wavelet transform.Secondly,the algorithm uses the neural network classifier based on resilient back propagation(RPROP)algorithm to classify the digital modulation signals.The simulation results show that the recognition rate can be kept above 95 % when the SNR is 2 dB or more.