Research On Parameter Estimation And Modulation Recognition Technology Of Digital Modulation Signal

The technology of the communication signal modulation identification is particularly critical in information countermeasure and reconnaissance technology.In the process of modulation identification,some parameters are sometimes needed to assist,so the importance of communication signal parameter estimation technology is self-evident.Modulation pattern identification technology mainly identifies the modulation pattern of the signal,while parameter estimation technology is to estimate the parameter of the signal,which can be used in the subsequent signal processing.In information confrontation and reconnaissance technology,these two technologies are very important.So the purpose of this paper is to study the modulation identification and parameter estimation method,and to implement a real-time identification and estimation system.Then the parameter estimation algorithm is studied,including the carrier frequency and the symbol rate of the signal.The carrier frequency estimation mainly uses the method based on the frequency domain and the method based on the wavelet ridge.Frequency-domain-based methods include frequency-domain centralization method and improved method combined with Welch power spectrum.The improved method can normalize the mean square error below 0.001 when the signal-to-noise ratio is greater than 0dB,and expand the application scope of the algorithm.Based on the wavelet ridge method,an improved method is proposed to optimize the initial iteration values and results,so that the error can be less than 0.001 when the signal-to-noise ratio is greater than-3dB.The estimation of symbol rate is mainly based on cyclic spectrum and wavelet transform.The estimation error of the method based on wavelet transform is less than that of the method based on cyclic spectrum under each SNR,and it can reach below 0.001 when SNR is greater than-5dB.And then the modulation identification of communication signal is studied.The method based on threshold decision and support vector machine classifier are used for classification.The method based on threshold judgment uses five features,and can recognize eight signals.The recognition rate is over 90% when the signal-to-noise ratio is greater than 15 dB.The method based on support vector machine can recognize 11 signals and the recognition rate is over 90% when the signal-to-noise ratio is greater than-2dB.Finally,a self-designed data acquisition and verification system based on software radio platform is studied.The system can transmit and receive more than 20 kinds of communication signals.On this basis,it can estimate the parameters of 11 kinds of digital modulation signals and identify the modulation modes of 8 kinds of digital signals.The estimation and identification algorithms used in this paper are all low-complexity methods in the above-mentioned algorithms.