Abstract
To overcome the performance degradation in the presence of steering vector mismatches, strict restrictions on the number of available snapshots, and numerous interferences, a novel beamforming approach based on nonlinear least-square support vector regression machine (LS-SVR) is derived in this paper. In this approach, the conventional linearly constrained minimum variance cost function used by minimum variance distortionless response (MVDR) beamformer is replaced by a squared-loss function to increase robustness in complex scenarios and provide additional control over the sidelobe level. Gaussian kernels are also used to obtain better generalization capacity. This novel approach has two highlights, one is a recursive regression procedure to estimate the weight vectors on real-time, the other is a sparse model with novelty criterion to reduce the final size of the beamformer. The analysis and simulation tests show that the proposed approach offers better noise suppression capability and achieve near optimal signal-to-interference-and-noise ratio (SINR) with a low computational burden, as compared to other recently proposed robust beamforming techniques.
Highlights
As one important branch of modern array signal processing, the beamforming technique has been widely studied and applied in the radar, wireless communication, sonar, medical imaging, as well as astronomy domains
A robust adaptive beamforming, based on the worst-case performance optimization, would delimit the uncertainty set of steering vectors by upper bounding the norm of the steering vector mismatch [8]
If certain observations are known during the procedure of training parameters, according to the minimum mean square error (MMSE) criterion, the complex vector of beamformer weights w can be described as: w = R −1p where, R is M × M covariance matrix, and p is the cross-correlation between the desired output and the received signal
Summary
As one important branch of modern array signal processing, the beamforming technique has been widely studied and applied in the radar, wireless communication, sonar, medical imaging, as well as astronomy domains. Neither the mismatch vector nor its upper bound is known in practice To overcome this model defect in standard DL algorithm, an adaptive beamforming method was developed, which estimates iteratively the difference between the actual and presumed steering vectors in order to maximize the output signal-to-noise plus interference ratio (SINR) [9,10,11]. This paper presents a new LS-SVR-based approach to address the robust beamforming issue This approach alleviates the array output SINR degradation in the presence of steering vector mismatches, strict restrictions on the number of available snapshots, and numerous interferences by replacing the conventional linearly constrained minimum variance cost function with a squared-loss function, and achieves better generalization capacity by applying Gaussian kernels to the array observations. A summary conclusion is given at the last of this paper
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