Research On Modulation Recognition And Parameter Estimation Technology Of Digital Phase Modulation Signals

Modulation recognition is one of the key technologies for communication reconnaissance and software radio communication.It plays a very important role in military and civilian fields.With the rapid development of modern communication and signal processing technology,communication systems and signal modulation schemes are becoming more and more complex.The modulation recognition of communication signal is facing increasingly severe challenges because of the complex communication environment.How to improve the recognition rate of the signal under low SNR(Signal-to-noise ratio)conditions and limited number of symbols is an important research direction for modulation recognition.In face of increasingly complex electromagnetic environment and increasingly scarce spectrum resources,researchers are tring to design modulation schemes with high spectral efficiency and strong resistance to interference.The evolution of BPSK,QPSK,OQPSK,SOQPSK,CPFSK and GMSK modulation schemes have attracted more and more attention in pursuit of excellent spectral and power efficiency.In this context,this thesis studies the modulation identification and corresponding parameter estimation of these digital modulation schemes.Firstly,this thesis systematically introduces the principle of digital phase modulation signals,analyzes and summarizes the evolution and relationship among them.However,the high-order theoretical accumulation and various spectral characteristics of different modulation schemes are analyzed.Secondly,a decision tree classification algorithm based on differential phase high-order cumulant is improved to overcome the low recognition rate of {QPSK,OQPSK} and {SOQPSK,CPFSK} baseband signals under low SNR conditions.In this improved algorithm,the highorder cumulant feature and the differential high-order cumulant feature are extracted after preprocessing to carry out modulation identification for low SNR scenarios.Another decision-tree classification algorithm based on spectral correlation and high-order cumulant feature is also proposed for signals with low SNRs.The effectiveness and efficience of these two algorithm are both verified via experimental simulation.Thirdly,besides the aforementioned decision-tree recognition approaches,a recognition algorithm based on SVM is proposed as well.The annular statistics of differential phase is used as the classification feature,and SVM is used as an intelligent classifier to realize modulation recognition.Finally,for the signal modulation parameter estimation,spectral characteristics and quadratic wavelet transform feature are used to estimate the carrier frequency and symbol rate estimation in this thesis.A joint estimation algorithm for modulation parameters based on cost function is proposed to estimate the modulation index,modulation order,carrier frequency and symbol rate of CPFSK signals with the cyclostationary property of CPFSK.