| The joint estimation of signal frequency and Direction Of Arrival(DOA)is a crucial technology in the field of array signal processing,and it plays a vital role in various domains such as radar,communication,and electronic countermeasures.With the continuous advancement of communication technology,the receiver must possess a higher rate of analog-to-digital converter to comply with the Nyquist sampling theorem,which leads to increased complexity in receiver hardware implementation.Additionally,to enhance the array antenna’s processing performance,an increased number of antennas are required,which also escalates the hardware cost.Therefore,sub-Nyquist sampling in both the time and space domains is necessary to reduce the antenna scale and the signal’s sampling rate.This thesis aims to realize the joint estimation of signal frequency and DOA with a small number of observation samples in scenarios characterized by sparsity in the time and space domains.The main research objective of this Thesis is to investigate the following issues:1.This thesis investigates a multi-coset-based space-time joint sparse array sampling structure in the scenario of a one-dimensional space-time-frequency-space sparse linear array.The compressed sensing theory is combined with a sparse array to configure a multicoset compressed sampling structure at the first array element to achieve frequency estimation of the signal.The remaining sparse arrays are then utilized to realize DOA estimation of the signal.Based on this structure,two-step parameter estimation algorithms based on MUSIC and OMP methods are adopted,respectively.The two-step parameter estimation algorithm based on MUSIC first employs the received signal obtained through multiple coset structure under-sampling to construct a spectral function regarding frequency and estimate the frequency parameter of the signal.Then,by combining the received signals of other array elements,the combined spectrum function of frequency and azimuth is constructed to estimate the DOA corresponding to the frequency.On the other hand,the two-step parameter estimation algorithm based on OMP exploits the signal’s sparsity in the frequency domain,uses the multi-coset structure to construct a compressed sensing model in the time domain,reconstructs the signal and achieves frequency estimation.Subsequently,based on the sparsity of the airspace signal in the airspace,the compressed sensing model of the airspace is constructed,and the DOA corresponding to the estimated frequency is estimated.2.In the two-dimensional time-frequency-space joint sparse L-array scenario,a subNyquist sampling array structure based on elastic delay is studied.Compared with the multi-coset sampling structure,this sampling structure has a looser setting of delay parameters on the array elements.Cross-correlation operations are performed between different time points on the two axes of the L array and between different array elements,and multiple cross-correlation matrices are constructed into a third-order tensor,and the factor matrix containing the parameters to be estimated is obtained through tensor decomposition,and finally through the factor The spatial phase of the signal is obtained by matrix estimation,and the frequency and two-dimensional DOA of the signal are simultaneously estimated.3.In the scenario of two-dimensional time-frequency-space joint sparse array,the sparse array structure based on multi-level time-delay reception is studied.Joint parameter estimation is performed using a j oint parameter estimation algorithm based on compressed sensing for fourth-order tensor models.Firstly,the received signal after multi-level delay is constructed into a fourth-order tensor model,and the tensor model is compressed according to a certain compression ratio,and then the factor matrix containing the parameters to be estimated is estimated by tensor decomposition,and finally the factor matrix is obtained from the factor matrix The frequency and two-dimensional DOA of the signal are estimated in. |
