In this work, we propose a subspace-based algorithm for direction-of-arrival (DOA) estimation applied to the signals impinging on a two-level nested array, referred to as multistep knowledge-aided iterative nested MUSIC method (MS-KAI-Nested-MUSIC), which significantly improves the accuracy of the original Nested-MUSIC. Differently from existing knowledgeaided methods applied to uniform linear arrays (ULAs), which make use of available known DOAs to improve the estimation of the covariance matrix of the input data, the proposed Multi-Step KAI-Nested-MU employs knowledge of the structure of the augmented sample covariance matrix, which is obtained by exploiting the difference co-array structure covariance matrix, and its perturbation terms and the gradual incorporation of prior knowledge, which is obtained on line. The effectiveness of the proposed technique can be noticed by simulations focusing on uncorrelated closely-spaced sources.

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The performance of many parameter estimation algorithms used for direction finding and localization techniques depends on the accuracy of the signal covariance matrix estimate. For a small number of sensors, the commonly used sample covariance matrix estimation procedure may only provide a poor estimate of the unknown true covariance matrix. In scenarios with low signal-to-noise ratio, stationary and non-stationary signal sources, a more accurate estimate of the signal covariance matrix can be achieved by incorporating a priori knowledge about the direction of arrival (DOA) of dominant signals. In this paper, we combine the weighted sample covariance matrix and a weighted knowledge-aided (KA) covariance matrix. We present a KA-Conjugate Gradient (KA-CG) algorithm that processes the enhanced covariance matrix estimate. Simulation results show that the proposed KA-CG algorithm substantially improves the probability of resolution of unknown close sources in the system, especially at middle low signal-to-noise ratios (SNR), requiring a reasonable number of samples for this aim.

This work studies multiple-antenna wireless communication systems based on super-resolution arrays (SRAs). We consider the uplink of a multiple-antenna system in which users communicate with a multiple-antenna base station equipped with SRAs. In particular, we develop linear minimum mean-square error (MMSE) receive filters along with linear and successive interference cancellation receivers for processing signals with the difference co-array originating from the SRAs. We then derive analytical expressions to assess the achievable sum-rates associated with the proposed multiple-antenna systems with SRAs. Simulations show that the proposed multiple-antenna systems with SRAs outperform existing systems with standard arrays that have a larger number of antenna elements.

In this work, we present direction-of-arrival (DoA) estimation algorithms based on the Krylov subspace that effectively exploit prior knowledge of the signals that impinge on a sensor array. The proposed multi-step knowledge-aided iterative conjugate gradient (CG) (MS-KAI-CG) algorithms perform subtraction of the unwanted terms found in the estimated covariance matrix of the sensor data. Furthermore, we develop a version of MS-KAI-CG equipped with forward-backward averaging, called MS-KAI-CG-FB, which is appropriate for scenarios with correlated signals. Unlike current knowledge-aided methods, which take advantage of known DoAs to enhance the estimation of the covariance matrix of the input data, the MS-KAI-CG algorithms take advantage of the knowledge of the structure of the forwardbackward smoothed covariance matrix and its disturbance terms. Simulations with both uncorrelated and correlated signals show that the MS-KAI-CG algorithms outperform existing techniques.