Hht And Compressed Sensing Applications In Radar Echo Analysis Research

Not only in the military but also in civil field, the collection and analysis of airborne radar echo signal plays a crucial role in the navigation recognition, target detection and communication, some problems were not solved effectively, such as high sampling rate, mass storage, and the data transmission and processing speed. Based on the purpose of practical application, this paper put HHT (Hilbert-Huang Transform) and CS (Compressive Sensing) theory into the analysis of radar echo signal, the main research contents are as follows:Firstly, the paper introduced the background of civil aircraft maintenance researching, analyzed the research situation and development trend of airborne radar echo signal at home and abroad, expounded HHT theory, the process and characteristics of EMD (Empirical Mode Decomposition), and presented simply the wavelet threshold filtering method based on the HHT. Then, this article introduced advancement and development of CS theory and its processing method, including the sparse transformation, signal measurement, reconstruction algorithm, analyzed and verified the method of EMD, filtering based on HHT, CS upon speech signal and mechanical fault signal related to the aircraft project.Secondly, this article detailed the process of radar echo modeling, including target echo, noise and clutter modeling, and simulated these signals base on assumed parameters. The echo signal was processed by method of EMD and HHT, analyzed in time-frequency domain. A variety of filtering methods applied to this signal, the experiment result verified that Hilbert spectrum filtering method which based on wavelet threshold pretreatment, gain advantages about signal strengthen and information acquisition in time-frequency domain.Finally, this thesis proposed a method, which combined HHT and CS, applied to the real radar echo signal acquired by laboratory, analyzed its features in time domain, frequency domain and time-frequency domain. We conducted filtering and reconstruction experiments upon this signal in environment of different SNR, and compared and analyzed simulation results. According to the different characteristics among these IMF (Intrinsic Mode Function) signal, this thesis adopted different sparse matrixes, Gaussian random measurement matrix, OMP (Orthogonal Matching Pursuit) reconstruction algorithm to conduct CS simulation, and detailed the reconstruction error and its reasons. This method combines the advantages of HHT and CS, made the signal sparser, reduced the sampling frequency. The process of data compression, transmission and reconstruction will save storage resources and improve transmission efficiency.