【作者】 王威；

【导师】 高玉龙；

【作者基本信息】 哈尔滨工业大学 ， 电子与通信工程， 2020， 硕士

【摘要】 对于多频带信号,亚奈奎斯特采样结构中的参数估计和信号恢复已被广泛研究。在亚奈奎斯特采样结构中,调制宽带转化器(Modulatd Wideband Converter,MWC)因其处理频域连续多带信号较好,实现相对简单而被广泛研究。本文首先对于MWC的基础理论和原理进行介绍。在此基础上,我们对欠采样场景下的经典OMP算法和我们提出的基于稀疏贝叶斯的重构算法进行研究,分别分析了信噪比、通道数和稀疏度对信号恢复性能的影响。仿真结果表明,信噪比越高、通道数越多、稀疏度越高则信号的恢复性能越好。并且,在相同的参数条件下,稀疏贝叶斯算法的恢复性能优于经典OMP算法。接下来,为了解决欠采样结构下信号参数估计的问题,本文将均匀线性阵列(Uniform linear array,ULA)和MWC相结合,并对这种基于ULA的欠采样信号阵列从频域进行分析。以此结构为基础,采用基于盖尔圆的信源数量估计算法估计信号个数,同时利用基于PM的频率角度联合估计算法和基于JAFE的频率角度联合估计算法进行频率角度的联合估计。仿真结果表明,这三种算法都能很好的实现信号的参数估计,在相同的信号参数条件下,基于JAFE的联合估计算法优于基于PM的联合估计算法。然而,现有亚奈奎斯特采样结构的硬件存在复杂性过高的问题。本文结合压缩感知和阵列信号处理技术提出了一种极简的亚奈奎斯特采样结构。与传统的调制宽带转换器结构相比,我们删除了周期伪随机序列,并将调制宽带转换器的模拟滤波器变成了数字滤波器,大大的减少了硬件复杂度。更为重要的是,该结构不仅能够实现欠采样下信号的频率DOA联合估计,还能实现信号的恢复。最后的仿真结果表明,在相同的信号参数条件下,本文提出的信号恢复算法性能高于传统的OMP算法和稀疏贝叶斯算法。同样地,多参数联合估计的性能也优于传统的方法。更多还原

【Abstract】 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.更多还原