Widely linear (WL) robust adaptive beamforming exhibits superior performance by effectively leveraging the additional noncircularity information. However, existing studies focus solely on the noncircular (NC) impinging interferences, often overlooking main-lobe interferences and suffering from high computational complexity. To tackle these challenges, this article introduces a generalized WL sparse reconstruction (GWLSR) beamforming framework that addresses the general scenario where the impinging interferences consist of mixed circular and NC signals from a sparse reconstruction perspective. The framework considers two variants, GWLSR$_{1}$ and GWLSR$_{2}$, to accommodate circular and NC signal of interest, respectively. Within this framework, we can estimate the power of a larger number of interferences in the general scenario, supported by a root finding-based approach for direction-of-arrival (DOA) and NC phase (NCP) estimation. We then reconstruct the conjugate augmented interference-plus-noise covariance by leveraging the estimated DOAs, NCPs, and power associated with the interferences. The proposed beamformers are computational efficient as all the involved procedures can be formulated using close-form expressions. In addition, they can effectively suppress main-lobe interferences. Simulation examples are provided to illustrate the advantages of the proposed beamformers.


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    Title :

    Generalized Widely Linear Robust Adaptive Beamforming: A Sparse Reconstruction Perspective


    Contributors:
    Yue, Yaxing (author) / Zhang, Zongyu (author) / Shi, Zhiguo (author)


    Publication date :

    2024-10-01


    Size :

    1188885 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English