Enhanced and Generalized Coprime Array for Direction of Arrival Estimation

Owing to the large degrees of freedom and reduced mutual coupling by generating difference coarrays, nonuniform linear arrays have aroused great interest in direction of arrival estimation. Previous works have shown some improved sparse arrays, while few find the common features hidden within these structures. In this article, we define a generic-coarray concept to reveal the impacts of variable ranges and element spacing on the uniform degrees of freedom (uDOFs), by which the sufficient condition for the connected coarrays is derived. We then propose an enhanced and generalized coprime array (EGCA) structure from the generic-coarray perspective. We show that the closed-form expression for the range of uDOFs is a function of sensor numbers and interelement spacing. We prove that, by coarray extension and hole filling, the optimized EGCA possesses more uDOFs than the previous coprime arrays. Furthermore, EGCA also provides the minimum number of sensor pairs with small separation. Simulations verify the superiority of EGCA using the subspace-based algorithm.

Automated summary

Due to the generation of difference coarrays, nonuniform linear arrays have attracted great interest in direction of arrival estimation. In this article, the authors propose a generic-coarray concept to reveal the impacts of variable ranges and element spacing on uniform degrees of freedom (uDOFs), and derive the sufficient condition for connected coarrays. They also introduce an enhanced and generalized coprime array (EGCA) structure from the generic-coarray perspective. Through coarray extension and hole filling, EGCA possesses more uDOFs compared to previous coprime arrays and provides the minimum number of sensor pairs with small separation.