Research On Detection And Extraction Of Frequency Hopping Signal With Low SNR

Because of its many advantages,frequency hopping communication is favored both in civilian and military applications.In particular,it has strong anti-interference capability,multiple access networking capability,and anti-fading capability etc,which makes it a major communication method in the military.As the third party of military communication,the intercepted signal is generally weak and the noise is relatively strong.To successfully crack the hopping pattern of the frequency hopping signal and extract useful information from the enemy in a low SNR environment,it is necessary to detect the received signal and determine whether there is the frequency hopping signal in the obtained segment signal,which provide a basis for the following processing steps.After the frequency hopping signal is detected,it also need to be extracted from a complex environment to provide a clean frequency hopping signal for subsequent parameter estimation and de-hopping steps.Therefore,it is necessary to conduct further study in these two aspects.This thesis studies the detection and extraction of frequency hopping signal in a low SNR environment.In frequency-hop signal detection,the power spectrum cancellation is adopted to improve multi-hop autocorrelation detection firstly,which overcomes the disadvantages of multi-hop autocorrelation that cannot be detected in the presence of fixed-frequency interference and enhances the detection performance.After introducing three kinds of power spectrum estimation detection methods,the fluctuating power spectrum base is improved to further increase the accuracy of detection.Then the method of extracting feature quantity detection is studied,After removing all kinds of interferences in the mark state,remark the rest of the signal,their feature values are extracted by positioning each signal.Then the characteristics of the frequency-hopping signal are combined to do identification detection.Finally,the clustering algorithm detection is studied emphatically.Two clusters are performed on the duration and time of occurrence of the frequency hopping signal,and then the type of positioning is detected.Through simulation experiments,it is verified that the above proposed methods can detect the presence of frequency hopping signals in a low SNR environment.In the frequency-hopping signal extraction process,the advantage of connected-tagging extraction relative for traditional row-by-row statistical extraction is analyzed firstly.The clustering location extraction method is studied on the basis of connected-tagging,and the situation in which there is an interference signal with the same frequency hopping signal cycle is analyzed.Similarly specified frequency hopping signals need to be extracted when involve multiple frequency hopping signals.The area location extraction method was also studied.Besides,the frequency hopping signal was located and extracted by using an area difference to find the mutation point.The morphological extraction method for estimating the skip period is studied.After the initial removal of noise and interference,the time-frequency ridges of the remaining signals are extracted to estimate the skip period,which provides the basis for the selection of structural elements to be treated by morphology.Finally,this thesis focuses on the study of texture-based frequency-hopping signal extraction.Research was conducted on different situations when different interferences and noise exist.Through experimental analysis,it is verified that the above proposed method can extract frequency-hopping signal extraction at low SNR accurately.