Research On High Resolution Direction Finding Signal Processing Method Based On Sparse Array

Direction finding or Direction of Arrival(DOA)is one of the main research directions of array signal processing.It is widely used in civil and military fields such as wireless communication,radio astronomy,radar direction finding and electronic warfare.The DOA estimation have developed rapidly in the past 30 years.From the physical structure model of the received signal to the signal processing method,it is constantly developing in the direction of small computation,low signal-to-noise ratio,high resolution and low cost.Sparse array is a very popular array arrangement in recent years.The spacing of sparse array elements is greater than half wavelength.Under the same number of array elements,it is larger than the array aperture of conventional full array.A sparse array can also form a virtual array with a larger array aperture,which can estimate more than the number of elements of the signal source,with higher resolution,degree of freedom and estimation accuracy,thus greatly reducing hardware costs and computational complexity in practical applications.Therefore,the research on high resolution direction finding signal processing based on sparse array has high theoretical research value and application prospect.In this paper,the signal processing method under sparse array is studied,and the algorithm is improved for the resolution of the direction finding algorithm.Firstly,the classical DOA estimation method based on uniform array is introduced.Two subspace algorithm MUSIC and ESPRIT are introduced,and simulation is carried out by using MATLAB simulation software.Besides the spatial smoothing algorithm is introduced,which can restore the rank of the covariance matrix under the condition that the signal source is completely coherent or single-shot sampling.By reviewing the basic theoretical part of DOA estimation,it lays the foundation for further research.Then the concept of sparse array and model structure are introduced,focusing on the minimum redundant array,the smallest holes array and the coprime arrays and nested arrays developed in recent years.The ambiguity resolution method based on sparse array and the acquisition mechanism of virtual array are studied.The direction finding algorithms under the coprime array and nested arrays are also studied,there are spatial smoothing MUSIC,Toeplitz matrix reconstruction MUSIC and Toeplitz matrix reconstruction ESPRIT.Through the analysis of the three algorithms,both the spatial smoothing method and the Toeplitz matrix reconstruction method use only half of the virtual aperture,thus the angle estimation resolution has room for improvement.Aiming at the virtual aperture loss problem of the above algorithms,this paper proposes a U-ESPRIT algorithm based on overlapping effective aperture.The algorithm extends the effective aperture by overlapping covariance matrix,which further improves the resolution and degree of freedom of the algorithm.The accuracy has also improved.The algorithm combines the U-ESPRIT algorithm for signal processing in the real domain to a sparse array,which also simplifies the computational complexity of the algorithm.At the end of the paper,the detailed degree of freedom analysis is given,and the theoretical maximum value of the virtual array's degree of freedom is derived.The performance analysis of the algorithm is given by simulation.