Moving platform based coprime linear arrays (CLAs) have been studied in recent years. It is shown that by shifting a CLA a half wavelength, the resulting difference coarray can be regarded as the union of the difference coarray of the original CLA and its two shifted versions with one lag to the left and one lag to the right, respectively, filling the majority or all holes in the difference coarray. However, only the lags which are neighbors of holes are actually useful, while the shifts of the other lags generate lags which already exist, contributing then negligibly to the increase of the degrees of freedom (DOFs). In this letter, an improved CLA configuration for moving platform is proposed. By judiciously designing the sensor positions, all lags can be utilized to fill holes, consequently a difference coarray with more consecutive lags can be obtained. Compared with the original configuration, the proposed configuration can detect more sources with the same number of sensors and same length of array motion.

Coprime linear arrays (CLAs) have drawn lots of attention. Efforts have been made to reveal the hole locations in the difference coarrays of CLAs, based on which many methods have been proposed to fill the holes, lengthening the consecutive difference coarray part and increasing the effective degrees of freedom (DOFs). Compared with CLAs, two dimensional (2D) coprime planar arrays (CPAs) are more relevant to real applications. However, no closed-form expressions for the hole locations in the difference coarray of a CPA have been found in the open literature, and the advantage in terms of DOFs of CPAs has not been well explored. In this letter, the structure of the difference coarrays of CPAs is investigated and the exact expressions of all hole positions are provided. Then, a holesfilling method is proposed, by which the most critical holes in the difference coarray can be filled such that a difference coarray with more consecutive lags as well as higher effective DOFs can be obtained.

The direction-of-arrivals (DOA) estimation with an unfolded coprime linear array (UCLA) has been investigated because of its large aperture and full degrees of freedom (DOFs). The existing method suffers from low resolution and high computational complexity due to the loss of the uniform property and the step of exhaustive peak searching. In this paper, an improved DOA estimation method for a UCLA is proposed. To exploit the uniform property of the subarrays, the diagonal elements of the two self-covariance matrices are averaged to enhance the accuracy of the estimated covariance matrices and therefore the estimation performance. Besides, instead of the exhaustive peak searching, the polynomial roots finding method is used to reduce the complexity. Compared with the existing method, the proposed method can achieve higher resolution and better estimation performance with lower computational complexity.

Direction-of-arrival (DOA) estimation with a co-prime linear array, composed of two uniform linear arrays with inter-element spacing larger than half-wavelength of incoming signals, has been investigated a lot thanks to its high resolution performance. For better computational efficiency, one class of methods treats the co-prime linear array as two sparse uniform linear subarrays. From each of them, high-precision but ambiguous DOA estimation is obtained, and the ambiguities are eliminated according to the co-prime property. However, the existing methods of this kind suffer from the insufficient reliability and high complexity. In this paper, the potential problems associated to the DOA estimation with two co-prime subarrays are discussed, and a reliable and efficient DOA estimation method is proposed. For each subarray, the true DOAs are treated as their equivalent angles, and the pair matching of them is accomplished by exploring the cross-correlations between the equivalent signals associated with the equivalent angles. Compared with other existing methods, the proposed method is able to achieve a better estimation performance in all situations, in terms of accuracy and complexity.

Massive MIMO systems have attracted lots of attention, and its performance is closely related to the estimation of direction of arrivals (DOA) of incoming sources. The conventional DOA estimation algorithms like MUSIC and ESPRIT are not suitable for massive MIMO systems, due to the complexity brought by the large number of antennas. The DOA estimation algorithm named Propagator Method (PM) has been proved efficient in terms of computational complexity, because it does not require the eigenvalue decomposition (EVD) of the covariance matrix of the received signals. In this paper, a new version of PM is proposed. By partitioning the covariance matrix of the received signals, the data contaminated by the main contribution of noise can be avoided, and the reconstruction of the system model in not needed, so the computational complexity can be decreased significantly. Some numerical simulations are given to prove its efficiency.