Research And Implementation Of Key Technologies On Satellite Signal Monitoring System

With the development of satellite communication technology,satellite communication technology plays a more and more important role in the field of civil and military communications.In order to ensure the reliable transmission of satellite communication signals and meet the needs of national security strategy,it is of great significance to study the key technologies of satellite signal spectrum monitoring.However,under the frequency offset and low SNR,symbol rate estimation and modulation classification have low accuracy and high complexity in the satellite signal spectrum monitoring system.In this case,we designed the overall architecture of the satellite signal monitoring system,then mainly studied the symbol rate estimation and modulation classification,which are the key technologies in the satellite signal monitoring system,and implemented a satellite signal automatic monitoring system.The main research tasks in this paper are as follows:1.We deeply studied the symbol rate estimation algorithm under frequency offset and low SNR,and proposed an improved envelope rate method.First,through theoretical analysis,we got the conclusion that the estimation accuracy of the cyclic spectrum method and the wavelet transform method were affected by the frequency offset,and computer simulation results show that the estimation accuracy of wavelet transform method is the most sensitive to frequency offset.Secondly,the theory of rate signal method for estimating symbol rate was derived theoretically,and an improved spectrum refinement algorithm was proposed.Compared with CZT algorithm,this algorithm has lower complexity.Finally,an improved envelope rate algorithm was proposed and through computer simulation,the results show that the envelope rate method has the highest estimation accuracy and low complexity when compared with delay-multiply circuit,cyclic spectrum method,wavelet transform method and rate signal method.2.The modulation classification algorithm for 10 kinds of common digital modulation signals under frequency offset was studied,and the improved quasi high power square spectrum characteristics of digital modulation signals are proposed.Firstly,the influence of frequency offset on high-order cumulants was analyzed theoretically,and the high-order cumulants features that were insensitive to frequency offset was given.Secondly,the quasi high power spectral characteristics of digital modulation signals were analyzed theoretically,which had the lower complexity than high power spectrum.Computer simulation results show that the quasi high power spectrum not only maintains the spectral characteristic of the high power spectrum,but also has higher detection probability.Finally,on the basis of this,the robust characteristic set for frequency offset was given,and the decision tree and random forest were used as classifier respectively for modulation classification.The simulation results show that the average classicifation correct probability of the 10 digital modulation signals is greater than 96% with algorithm based on the decision tree when the SNR is higher than 7d B.The average classication probability of the algorithm based on random forests is greater than 96% when the SNR is greater than 5d B,which has higher classification accuacy,compared with the algorithm based on decision tree method.3.The satellite signal monitoring system designed int this paper was implemented by USRP X310 and GPU with software programming.First,the hardware and software structure and function of the system were given.The main functions of the system include spectrum display,carrier parameter estimation,symbol rate estimation,modulation classification and constellation display.Then the system was tested,and the test results show that the satellite signal monitoring system implemented in this paper can meet the expected functional requirements and performance indicators.