Abstract
In this paper, a novel and robust algorithm is proposed for adaptive beamforming based on the idea of reconstructing the autocorrelation sequence (ACS) of a random process from a set of measured data. This is obtained from the first column and the first row of the sample covariance matrix (SCM) after averaging along its diagonals. Then, the power spectrum of the correlation sequence is estimated using the discrete Fourier transform (DFT). The DFT coefficients corresponding to the angles within the noise-plus-interference region are used to reconstruct the noise-plus-interference covariance matrix (NPICM), while the desired signal covariance matrix (DSCM) is estimated by identifying and removing the noise-plus-interference component from the SCM. In particular, the spatial power spectrum of the estimated received signal is utilized to compute the correlation sequence corresponding to the noise-plus-interference in which the dominant DFT coefficient of the noise-plus-interference is captured. A key advantage of the proposed adaptive beamforming is that only little prior information is required. Specifically, an imprecise knowledge of the array geometry and of the angular sectors in which the interferences are located is needed. Simulation results demonstrate that compared with previous reconstruction-based beamformers, the proposed approach can achieve better overall performance in the case of multiple mismatches over a very large range of input signal-to-noise ratios.
Highlights
T O enhance the desired signal arriving from the target direction while suppressing interfering signals from other directions, adaptive beamforming techniques have been widely applied in radar, sonar, seismology, radio astronomy, medical imaging, wireless communications, and other fields [1]
The paper’s contributions can be summed up as follows: 1) We propose REC-discrete Fourier transform (DFT) to reconstruct the noise-plus-interference covariance matrix (NPICM) using the autocorrelation sequence of a random process estimated from a set of measured data with low computational complexity
This paper introduces a new approach for uniform linear array (ULA) in order to reconstruct the NPICM based on DFT coefficients of the auto correlation sequence of the measured data
Summary
T O enhance the desired signal arriving from the target direction while suppressing interfering signals from other directions, adaptive beamforming techniques have been widely applied in radar, sonar, seismology, radio astronomy, medical imaging, wireless communications, and other fields [1]. The NPICM in [12] is reconstructed based on the Capon spectral estimator by integrating over an angular sector that excludes the DoA of the SOI, while the desired signal SV is estimated by solving a quadratically constrained quadratic programming (QCQP) problem This method shows reasonable performance, but is sensitive to large DoA mismatches [13], [14]. In order to avoid this problem, a very recent algorithm in [25] was proposed based on the reconstruction of the NPICM and DSCM In this method, all interference powers as well as the desired signal power are estimated using low complexity principle of maximum entropy power spectrum. 3) The proposed REC-DFT method is simple to implement, and it is shown through numerical examples to yield good beamformer performance against different mismatches at high and low SNRs
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