Parameter Estimation Of Frequency Hopping Communication Signals

Frequency hopping (FH) communication system has been widely used in areas such as secure communication, because of its outstanding anti-jamming capability and anti-intercept ability. As the side of communication confrontation, how quickly and effectively intercepted and handle the hopping information of the communications side has become a key and difficult point in the present study. This article based on the time-frequency analysis and the theory of Particle Swarm Optimization (PSO), and combined with the Link-16data to achieve the detection, parameter estimation, and modulation recognition of the frequency hopping signal. The works of this thesis are shown below:(1) Analysis of the characteristics of the fix frequency signal and the burst signal in the complex signal in the frequency hopping signal. According to the characteristics of the signal shown in the time-frequency plane, calculate the signal in the time domain and frequency domain of the ridge line, separate the frequency hopping signal from the complex signal, improve the detection accuracy of the frequency hopping signal.(2) Study the Short-Time Fourier Transform (STFT), Spectrogram, Wigner-Willie Distribution (WVD), Pseudo Wigner-Willie Distribution (PWVD) and Smoothed Pseudo Wigner-Willie Distribution (SPWVD) in the frequency hopping signal detection and parameter estimation in the application. Compare the estimation accuracy of parameter in the different SNR of Spectrogram and SPWVD, the result is that the method of time-frequency analysis has a serious issue of SNR threshold.(3) Study the Genetic Algorithm (GA) and the Particle Swarm Optimization (PSO) to solve the problem of that the method of time-frequency analysis has a low estimation accuracy of parameter in the low SNR. The simulation results show that the PSO has a superiority of frequency hopping signal parameter estimation in low SNR.(4) Use the signal’s spectrum and higher order spectral information to realize the modulation recognition of Link-16signalFinally, the simulation results show the effectiveness of the methods of time-frequency analysis and PSO. The results show that the time-frequency analysis method can effectively achieve the frequency hopping signal detection and parameter estimation. At low SNR, The PSO can achieve a precise estimate of effect on the parameters of frequency hopping signals.