Spectrum Sensing And Parameter Estimation Method Based On Subnyquist Sampling Structure

For multi-band signals,parameter estimation and signal recovery in the sub-Nyquist sampling structure have been extensively studied.In the sub-Nyquist sampling structure,the Modulated Wideband Converter(MWC)is relatively simple and widely studied because it processes continuous multi-band signals in the frequency domain.This article first makes a research on the basic theories and principles of MWC.On this basis,we simulated two undersampling signal reconstruction algorithms,one is the classic OMP algorithm,and the other is the proposed reconstruction algorithm based on sparse Bayes.For these two algorithms,we simulated the error comparison of signal recovery under different signal-to-noise ratios,different channel numbers and different sparsity.Experimental results show that the higher the signal-to-noise ratio,the greater the number of channels,and the higher the sparseness,the better the signal recovery performance,and under the same parameter conditions,the recovery performance of the proposed sparse Bayesian algorithm is better than the traditional The OMP algorithm.Next,in order to solve the problem of signal parameter estimation under the undersampling structure,this paper combines a uniform linear array(ULA)and MWC,and does a theoretical analysis of the spectrum of this undersampling array based on ULA.Then on this basis,the number of sources estimation algorithm based on Gaelic circle,the frequency-angle joint estimation algorithm based on PM and the JAFE-based joint estimation algorithm are given.Then the algorithm is simulated,and these three algorithms are applied to the proposed undersampling structure based on ULA.Simulation results show that these three algorithms can achieve good signal parameter estimation.Under the same signal parameter conditions,the joint estimation algorithm based on JAFE is better than the joint estimation algorithm based on PM.However,the hardware of the existing sub-Nyquist sampling structure has a problem of excessive complexity.In this paper,a simplified sub-Nyquist sampling structure is proposed by combining compressed sensing and array signal processing technology.Compared with the traditional modulated broadband converter structure,we have removed the periodic pseudo-random sequence and changed the analog filter of the modulated broadband converter into a digital filter,which greatly reduces the hardware complexity.More importantly,this structure can not only realize the joint estimation of the frequency DOA of the signal under sampling,but also realize the signal recovery.The final simulation results show that under the same signal parameters,the performance of the signal recovery algorithm proposed in this paper is higher than the traditional OMP algorithm and sparse Bayesian algorithm.Similarly,the performance of multi-parameter joint estimation is better than traditional methods.