Low Complexity Direction Of Arrivals Estimation Methods Based On Unfolded Coprime Linear Arrays

Array signal processing has been widely used in the fields of radar,sonar,mobile communication and ultrasonic imaging.Direction of arrival(DOA)is the most important research branch in the field of array signal processing.The array structure is the basis of the DOA estimation.At present,the most commonly used is the uniform linear array(ULA).However,to improve the estimation performance of ULA,which need increasing the number of antennas to obtain the larger aperture.Compared to ULA,coprime linear array(CLA)has larger aperture at the same number of antennas,resulting in higher estimation performance at a lower cost.At present,the DOA estimation based on the CLA is mainly divided into two categories.One is the difference-coarray-based method,which attempts to virtualize the structure of the uniform array with the CLA,thereby increasing the degree of freedom,but this method requires a large number of snapshots,and the calculation is extremely complicated.The other is the subarray-based method,which decomposes a CLA into two uniform subarrays,and then we can use the estimation method of ULA.This method greatly reduces the complexity and has more practical value,however,its degrees of freedom(DOFs)is limited by the subarray,and the ambiguity angles are generated.A new array structure was proposed,which is the unfolded coprime linear array(UCLA).This array structure can be estimated by the MUSIC method for the DOA estimation,currently.Compared with the subarray-based method of the traditional CLA,it has complete DOFs,anbiguity-free angles and other advantages.Through further research,this paper finds the shortcomings of the above methods: Firstly,the complexity of MUSIC method is too high;secondly,compared with the decomposition of the traditional CLA into two uniform subarrays,the UCLA losses the uniform property,most traditional methods of DOA estimation cannot be directly applied with UCLA.MUSIC method needs to perform peak search with the full angle domains,which leads to the high complexity.This paper proposes to use the root-MUSIC method with a UCLA.The root-MUSIC method does not need to perform spectral peak search,which effectively reduces complexity without losing the estimated performance.Due to UCLA losses the uniform characteristic,in this paper,Toeplitz reconstruction was performed on the self-covariance matrix,and it is combined with the root-MUSIC method for DOA estimation.The complexity calculation and simulation results show that the proposed method has highresolution and low complexity.For the MUSIC method,the complexity is sharp when the number of antennas is large.To solve this problem,blocking the covariance matrix,constructs the orthogonal operator to replace the eigenvalue decomposition of the covariance matrix,which avoids the mathematical decomposition of the covariance matrix,effectively reduces the complexity.The complexity calculation and simulation results show that this method achieved a balance between estimation performance and complexity.