Interpolating Coprime Arrays With Translocated and Axis Rotated Compressed Subarrays by Iterative Power Factorization for DOA Estimation

Abstract

In this paper, a novel array structure exploiting coprime arrays is proposed, which comprises the translocation of one subarray and axis rotation with a compression of another subarray to produce a larger number of consecutive lags. This composition is named CATARCS, which stands for coprime array with translocated and axis rotated compressed subarrays. The scheme offers consecutive co-array lags in remarkable number $4MN-1$ by using only $2M+N-1$ number of sensors attaining higher degrees of freedom than the state of the art, where $M$ and $N$ are coprime with congenial interelement spacings. However, a very few holes still exist in the co-array, which can be filled up through interpolation. In this paper, the iterative power factorization (IPF) algorithm has been demonstrated as a tool for incremented-rank-IPF matrix recovery which is unostentatious. No extra tuning parameter is required for this approach and several undesired issues of other techniques can be reconciled by this. The main contribution of this paper is suitable modification of array geometry arrangements to increase consecutive lags and to interpolate via IPF, which is used first time for direction of arrival (DOA) estimation in order to exhibit better performances in terms of detecting and resolving a larger number of sources by enhancing resolution. Later, DOA estimation can be achieved by performing multiple signal classification (MUSIC) algorithm. Simulation results validate its effectiveness by achieving lower cost, faster, and accurate DOA estimation even in lower angular separation of sources compared with other techniques like spatial smoothing MUSIC, co-array the least absolute shrinkage and selection operator, spline, iterative co-array array interpolation, and Toeplitz completion.