Research On Analog-to-Information Conversion Based On Pseudo-Random Modulation

The traditional signal sampling needs to meet the Nyquist sampling rate,which means that sampling rate is two times of the signal bandwidth.Nyquist criterion is the sufficient condition for the reconstruction of the signal.With the development of the future bandwidth and wideband communication technology,the signal sampling,transmission and reconstruction based on the traditional sampling theory is facing severe challenges.Compressed Sensing(CS)is proposed to sample the compressive signal below Nyquist rate,and achieves accurate reconstruction by solving an optimization problem.Analog-to-information conversion(AIC)is based on compressed sensing theory,it can directly sample the analog signals by sub-Nyquist sampling rate,and with high probability of reconstructed accurately.In this paper,we study the reconstruction algorithm of compressed sensing,and improve the reconstruction performance by modify the algorithm.Besides,we study the sampling and reconstruction of Linear Frequency Modulated signal as the research object.The main work is as follows:Firstly,the performance of reconstruction algorithm is studied in this paper.We mainly modify the SAMP construction algorithm.Combine with Dice matching coefficient,DSAMP algorithm is proposed as an improvement of SAMP algorithm.The improved algorithm can improve the reconstruction effect in some degree.Secondly,this paper studies the feasibility of sampling and reconstruction of Linear Frequency Modulated(LFM)by using AIC sampling method,and optimizes the reconstruction effect.In this paper,we use the energy accumulation of the LFM signal in the fractional Fourier transformation domain to construct the sparse basis to represent the signal.The key parameters of LFM are extracted by parameter estimation method,and then the sparse dictionary is generate,and the sparse degree of the signal is further reduce by the idea of the swept frequency fileter,so as to improve the reconstruction performance...