Abstract
In this paper, we propose an adaptive beamforming algorithm for large uniform linear arrays (ULAs), where only a nested subarray is utilized to calculate the beamforming coefficients for the original ULA. In this algorithm, the steering vectors and powers of the signal-of-interest (SOI) and interferences are firstly estimated using the Capon spatial spectrum and known array structure, and the interference-plus-noise covariance matrix (INCM) is then constructed. Subsequently, an augmented INCM is formed via vectorization and spatial smoothing operations. Finally, the beamformer weight vector is determined by the augmented INCM and the estimated SOI steering vector. Our proposed algorithm exploits the enhanced degrees of freedom of the nested array, and thus can be applied to a large ULA to reduce the implementation complexity. Moreover, it fundamentally eliminates the SOI component. Numerical results demonstrate that the proposed algorithm performs better than the existing approaches.
Highlights
In array signal processing, adaptive beamforming is a fundamental technology due to wide applications, e.g., in radar, sonar, wireless communications, cognitive radio networks, medical imaging [1]–[3]
The standard minimum variance distortionless response (MVDR) beamformer is quite sensitive to the mismatch between the actual steering vector and presumed one, which is caused by various imperfections such as look direction errors and local scattering effects
CONVERGENCE RATES OF COVARIANCE MATRICES In the first experiment, we examine the convergence rates of the sample covariance matrix (SCM), spatially smoothed matrix (SSM) and augmented INCM (AINCM)
Summary
Adaptive beamforming is a fundamental technology due to wide applications, e.g., in radar, sonar, wireless communications, cognitive radio networks, medical imaging [1]–[3]. The beamformers of [38] and [51] do not consider various imperfections such as look direction error and coherent local scattering, and they are very sensitive to the model mismatch To tackle this problem, Yang et al [53] proposed a robust adaptive beamformer, where the interference-plus-noise covariance matrix (INCM) is reconstructed by projecting the SSM into the interference subspace, while the SOI steering vector is estimated by solving a convex optimization problem. By constructing an augmented INCM, the proposed algorithm exploits the enhanced DOFs of the nested array, and fundamentally removes the SOI component It achieves better performance than the existing schemes in high SNR regions.
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