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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.

In this paper, a novel array structure exploiting coprime arrays is proposed which can be very proficient to determine the number of consecutive lags in proportion with the number of array elements. The proposed method comprises novel array structure by configuring three subarrays positioned in alignment with some prescribed values. By increasing array elements in third subarray while keeping other subarrays fixed, explicit number of consecutive lags could be obtained proportionately. The proposed method offers maximization of consecutive lags in remarkable number by calculating the fourth order difference co-array unifying interpolation. The forth order difference co-array is achieved by exploiting the second order difference co-array twice. The consideration of third subarray in addition with two coprime subarrays leads to a novel array structure which can significantly enhance degrees of freedom. An effective interpolation technique nuclear norm minimization is considered to fill the holes subsisting in the virtual co-array in order to exploit full virtual co-array length. This interpolation method uses convex framework which is trackable and very simple to implement yielding a freedom of fixing any predefined extra tuning parameter. The proposed method in this paper is named as VEFODCI which stands for virtual extension of coprime arrays by exploiting fourth order difference co-array with interpolation. The sparse Bayesian learning is used for direction of arrival (DOA) estimation exploiting the proposed novel array structure by imposing interpolation to fill the holes. The array geometry of sensor distributions proves that the proposed method is less susceptible from mutual coupling effect. The simulation results stipulate that the proposed method is performing DOA estimation accurately even in lower angular separation of sources by achieving larger number of consecutive lags than the state of the art.