Recently, nested and coprime arrays have attracted considerable interest due to their capability of providing increased array aperture, enhanced degrees of freedom (DOFs), and reduced mutual coupling effect compared to uniform linear arrays (ULAs). These features are critical to improving the performance of DOA estimation and adaptive beamforming. In this paper, a new sparse array configuration based on the maximum inter-element spacing constraint (MISC) is proposed. The MISC array configuration generally consists of three sparse ULAs plus two separate sensors that are appropriately placed. The MISC array configurations are designed based on the inter-element spacing set, which, for a given number of sensors, is uniquely determined by a closed-form expression. We also derive closed-form expressions for the number of uniform DOFs of the MISC arrays with any number of sensors. Compared with the existing sparse arrays, the MISC array enjoys two important advantages, namely, providing a higher number of DOFs and reducing the mutual coupling effects. Numerical simulations are conducted to verify the superiority of the MISC array over other sparse arrays.

In this letter, we devise an efficient approach for estimating the directions-of-arrival (DOAs) of coherent signals using coprime arrays. Specifically, we firstly derive an augmented uniform linear array (ULA) via coprime array interpolation. Subsequently, we define a Toeplitz matrix that is formed using the correlation information of the interpolated ULA theoretical outputs, and recover the Toeplitz matrix by solving a nuclear norm minimization problem. After the low-rank Toeplitz matrix is recovered, the coherent signals are well resolved by the MUSIC algorithm. Simulation results demonstrate the advantages of the proposed

To ensure link reliability and signal receiving quality, robust adaptive beamforming (RAB) is vital important in mobile communications. In this paper, we propose a new RAB algorithm based on interference-plus-noise covariance (INC) matrix reconstruction and steering vector (SV) estimation. In this method, the INC matrix is reconstructed by estimating all interferences SVs and powers, as well as the noise power. The interference SVs are estimated by using the Capon spatial spectrum together with robust Capon beamforming principle, subsequently the interference powers are estimated based on the orthogonality between different signal SVs. On the other hand, the desired signal SV is estimated via maximizing the beamformer output power by solving a quadratic convex optimization problem. The proposed algorithm only needs to know in advance the array geometry and angular sector in which the desired signal lies. Simulation results indicate that the proposed algorithm outperforms the existing RAB techniques in terms of the overall performance in cases of various mismatches.

Nested arrays have recently attracted a growing concern owing to their ability of achieving extended aperture and enhanced degrees of freedom (DOFs) compared to conventional uniform linear arrays (ULAs). In this paper, we devise a source localization scheme under the simultaneous existence of nearfield (NF) and far-field (FF) sources using a symmetric double nested array (SDNA). Firstly, the direction-of-arrivals (DOAs) of FF sources are estimated using one-dimensional (1-D) MUSIC spectrum. Then, the NF components can be extracted by applying the oblique projection technique. By constructing a special NF cumulant matrix and performing vectorization to generate the virtual array signal, the DOAs of NF sources can be estimated by the spatial smoothing MUSIC (SS-MUSIC) algorithm. Finally, with the NF DOA estimates, the range estimates of NF sources are obtained via 1-D peak searching. We also derive the consecutive range of the difference coarray and optimum array configurations for the SDNA, under a given number of sensors. The proposed algorithm exploits the large coarray aperture to enhance the localization performance and can distinguish correctly the types of the sources. Numerical results demonstrate that our proposed method significantly outperforms the existing methods for both DOA and range estimations.

Adaptive beamformer is very sensitive to model mismatch, especially when the signal-of-interest (SOI) is present in the training data. In this paper, we focus on the topic of robust adaptive beamforming (RAB) based on interference-plus-noise covariance matrix (INCM) reconstruction. Firstly, we analyze the effectiveness of several INCM reconstruction schemes, and particularly analyze the impacts of interference power estimation on RAB. Secondly, according to the analysis results, we develop a simplified algorithm to estimate the interference powers, and a RAB algorithm based on INCM reconstruction is then presented. Compared with some existing methods, the proposed algorithm simplifies the interference power estimation of INCM reconstruction. Aligned with our analysis, simulation results demonstrate that the overestimation of interference powers hardly degrades the performance of adaptive beamforming, and our proposed algorithm achieves nearly optimal performance across a wide range of signal-to-noise ratio (SNR).

As is well known, nonuniform linear arrays have significant advantages in array aperture and degrees of freedom over uniform linear arrays. Using their difference coarrays, subspace-based approaches can be utilized to perform underdetermined and high-resolution direction-of-arrival (DOA) estimation. However, the subspace-based approaches depend on the covariance matrix reconstruction in the coarray domain, which are not statistically efficient when the number of sources is more than one and less than the number of sensors. In this paper, to overcome this drawback, we devise an augmented covariance matrix reconstruction algorithm for DOA estimation in the coarray domain. The proposed algorithm recovers the complete augmented covariance matrix by solving a rank-minimization problem. But unlike the conventional schemes, it exploits the estimation error distribution of the incomplete augmented covariance matrix to derive the constraint condition of the rank-minimization problem. Based on the reconstructed augmented covariance matrix, we can enhance the DOA estimation performance for multiple source scenario at high signal-to-noise ratio. Although our algorithm is developed based on the non-consecutive coarray, it is also suitable for the consecutive coarray. Numerical results demonstrate the superiority of the proposed algorithm over several existing approaches.