Research On Signal Modulation Recognition And Anti-jamming Technology In Complex Environment

In the context of the rapid development of communication technology,accurate and efficient information interaction has become the basic demand of contemporary people for communication technology.With the increasing demand,communication technology has gradually broken through,modulation types have gradually increased,and communication quality has gradually improved,but this also makes the existing spectrum resources gradually scarce.In the case of tight spectrum resources,continuous phase signals with more energy aggregation emerge as the times require.They are favored because of their advantages of continuous phase,energy aggregation,and constant envelope.Therefore,most researchers derive some continuous phase signals from the existing discontinuous phase signals to improve the communication quality under limited spectrum resources.However,this also brings the following problems.On the one hand,discontinuous phase signal and continuous phase signal have high similarity in signal characteristics,and it is difficult to identify when feature extraction is fuzzy under low SNR.Failure to correctly identify signal types and extract prior information will cause great trouble to the demodulation and other parts of the communication system.On the other hand,in the case of dense spectrum resources,co-frequency interference has brought greater harm to the communication system.In order to ensure the safety and reliability of communication,more effective signal anti-interference algorithm has become an urgent task.Therefore,this thesis explores the signal recognition algorithm and signal anti-interference algorithm with better performance in complex communication environment from two aspects.In the mixed communication environment of continuous phase signal and non-continuous phase signal,the signal characteristics of continuous phase signal and non-continuous phase signal have high similarity,and cannot be effectively identified when the feature extraction is fuzzy.Among them,SOQPSK signal is a typical continuous phase signal evolved from PSK signal,which is similar to the spectral characteristics of PSK signal and FSK signal,and is difficult to identify.In order to solve the problem that it is difficult to identify due to the fuzzy feature extraction in the case of continuous phase and discontinuous phase mixing,this thesis proposes an identification algorithm based on blind digital receiver and goodness of fit test for the mixed signal set of continuous phase and discontinuous phase {SOQPSK,2FSK,BPSK,QPSK}.Firstly,the phase-locked loop in blind digital receiver extracts the signal feature.Then,the cumulative distribution function of the same phase branch is counted,and the goodness of fit test algorithm is used for preliminary classification.Finally,the identification of various signals is realized according to the characteristics of the signal such as in-phase branch information,orthogonal branch information and frequency change.The simulation results show that compared with the traditional algorithm using instantaneous amplitude spectrum for recognition,the proposed algorithm has better performance,and also has better recognition ability when the signal feature extraction is fuzzy at low SNR.In the complex environment with dense spectrum,the received signal is greatly affected by the same frequency narrowband interference,and the signal demodulation performance is seriously degraded.In order to ensure the safety and reliability of communication,it is necessary to suppress the interference of the received signal.The traditional unit cell anti-interference algorithm starts from the local domain characteristics of interference to carry out interference suppression.This anti-interference algorithm based on local domain feature extraction cannot reflect the overall characteristics of interference features.When the interference feature extraction is not accurate,the anti-interference performance of the system decreases.In this thesis,taking BPSK signal as the research object,a BPSK signal anti-jamming algorithm based on generative adversarial networks is proposed.The algorithm first constructs the signal by image,and constructs the mapping relationship between the interfered signal image and the undisturbed signal image by generating an adversarial network.The interference suppression problem is transformed into a distribution mapping problem,and the interference suppression is realized by building a precise distribution mapping relationship.The simulation results show that this signal anti-interference algorithm based on generative confrontation network has better interference suppression performance than the traditional frequency domain notch anti-interference algorithm.