Study The Method Of Under Sampling Flight Data Reconstruction Based On Compress Sensing

With the rapid development of information technology, the demand for information on the amount of data in today's society are increasing, which have a higher transmission speed requirements. As traditional information sampling compression uniform Nyquist sampling methods, it's easily lead to transmission of pressure and the loss of signal information.Therefore, as a rookie signal processing theory, compressed sending is gradually being applied to many fields. In the field of civil aviation, in order to satisfy the flight data storage efficient transmission, In this paper, a method based on compressive sensing theory will be presented, it use QAR B737 aircraft flight data for the study, based on flight data QAR different signal characteristics, analysis of the usefulness of reconstruction system with QAR data based on compressed sensing.Firstly, this paper take constraints perceived of compress sendsing as a theoretical basis mathematical model, use the instance of sparsity analysis for flight data example as compressed sensing experience basis. First, for the under-sampled QAR raw data, do high sampling rate interpolation preprocessing. Secondly, according to four different sparse groups like DCT group, Haar group, Db series group, Sym series groups, based on three experiments analysis of flight data sparsity, make the QAR flight data signal into two categories, and concluded that smooth change of flight data using DCT-based sparse way, non-smooth change of flight data using wavelet sparse way.Secondly,this paper presents the reconstructed signal processing model of flight data compression based on discrete cosine basis, and use smooth change of the signal for instance made a case study. Use discrete cosine basis for sparse groups, Gaussian random matrix as the measurement matrix, greedy algorithms OMP for reconstruction algorithm. Use RMSE error coefficient as criteria. Analyze with different compression ratio, the smooth change of flight data reconstruction performance in groups based on the discrete cosine compressive sampling,Comfirm the feasibility of this model in smooth change of flight data.Finally, this paper presents the reconstructed signal processing model of flight data compression based on wavelet, and use non-stationary signals change flight for instance analysis., Db10 as a base wavelet sparse group, Gaussian random matrix as the measurement matrix, the improved algorithm ROMP is used as the reconstruction algorithm. Use RMSE error coefficient as criteria. Analysis in different compression ratio, under single-layer and multi-layer wavelet sparse, the non-stationary change of flight data reconstruction performance, Comfirm the feasibility of this model in non-smooth change of flight data.