Sparse convolutional array for DOA estimation

Abstract

Array signal processing plays an important role in many areas. Besides the Uniform Linear Array, there are many sparse array that have been proposed, for example, Minimum-redundancy array, Co-prime array, nested array, etc. However, most of the array structures have certain disadvantages. A new type of nonlinear sparse sensor array called sparse convolutional array is illustrated which can reduce the number of physical sensors while remain a decent performance in DOA estimation. The sparse convolutional array contains three groups of physical sensors and is able to form a hole-free difference co-array. By adding sensors on two sides instead of the center, the proposed array shows improved performance compared to some other approaches while reminds few physical sensors. The array geometrical structure is illustrated, and the numerical result is provided. We also extended this array structure to two-dimensional case, and the performance is illustrated.