Improved Coprime Array Designs Based on Moving Platform for Direction of Arrival Estimation

In this article, we mainly propose two improved coprime arrays (CAs) on array motion, ImCAAM1 and ImCAAM2, for direction of arrival (DOA) estimation, which can generate a longer consecutive difference co-array. Both the designs can solve the problem that part of lags, not surrounded by holes in difference co-array, have invalid use, when considering moving sparse array. The first scheme primarily implements two operations: compressing the interelement spacing of one subarray and staggering a certain distance between two subarrays. Two operations make contributions to increasing consecutive degrees of freedom (cDOF). Whereas the second one is able to further fill the remaining holes located at both the ends of virtual array. With the interelement spacing compress factor, equal to the number of elements of another subarray, and re-staggering the distance between subarrays, we can eventually obtain a longer full-populated virtual array. Finally, numerical simulations are presented to verify the efficacy of the proposed sparse array geometries using the subspace-based DOA estimation algorithm in terms of cDOF, spatial spectrum, and DOA estimation accuracy.

Automated summary

In this article, two improved coprime arrays (CAs), ImCAAM1 and ImCAAM2, are proposed for direction of arrival (DOA) estimation, which can generate a longer consecutive difference co-array. Both designs solve the problem of invalid use of part of lags in difference co-array when considering moving sparse array. The first scheme involves compressing the interelement spacing of one subarray and staggering a certain distance between two subarrays to increase consecutive degrees of freedom (cDOF). The second scheme further fills the remaining holes at both ends of the virtual array by adjusting the interelement spacing and staggering distance between subarrays.