Study of Robust Adaptive Beamforming Based on Low-Complexity DFT Spatial Sampling

• URL: http://arxiv.org/abs/2106.12663v1
• Date: Wed, 23 Jun 2021 21:46:54 GMT
• Title: Study of Robust Adaptive Beamforming Based on Low-Complexity DFT Spatial Sampling
• Authors: Saeed Mohammadzadeh, Vitor H.Nascimento, Rodrigo C. de Lamare and Osman Kukrer
• Abstract summary: A novel and robust algorithm is proposed for adaptive beamforming based on the idea of reconstructing the autocorrelation sequence. A key advantage of the proposed adaptive beamforming is that only little prior information is required.
• Score: 26.82194157337935
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Adaptive posterior distributions for uncertainty analysis of covariance matrices in Bayesian inversion problems for multioutput signals [0.0]
  We address the problem of performing Bayesian inference for the parameters of a nonlinear multi-output model. The variables of interest are split in two blocks and the inference takes advantage of known analytical optimization formulas.
  arXiv  Detail & Related papers  (2025-01-02T09:01:09Z)
• Variable Substitution and Bilinear Programming for Aligning Partially Overlapping Point Sets [48.1015832267945]
  This research presents a method to meet requirements through the minimization objective function of the RPM algorithm. A branch-and-bound (BnB) algorithm is devised, which solely branches over the parameters, thereby boosting convergence rate. Empirical evaluations demonstrate better robustness of the proposed methodology against non-rigid deformation, positional noise, and outliers, when compared with prevailing state-of-the-art transformations.
  arXiv  Detail & Related papers  (2024-05-14T13:28:57Z)
• Weakly supervised covariance matrices alignment through Stiefel matrices estimation for MEG applications [64.20396555814513]
  This paper introduces a novel domain adaptation technique for time series data, called Mixing model Stiefel Adaptation (MSA) We exploit abundant unlabeled data in the target domain to ensure effective prediction by establishing pairwise correspondence with equivalent signal variances between domains. MSA outperforms recent methods in brain-age regression with task variations using magnetoencephalography (MEG) signals from the Cam-CAN dataset.
  arXiv  Detail & Related papers  (2024-01-24T19:04:49Z)
• Efficient Covariance Matrix Reconstruction with Iterative Spatial Spectrum Sampling [0.0]
  We propose a cost-effective technique for designing robust adaptive beamforming algorithms with iterative spatial power spectrum (CMR-ISPS) The proposed CMR-ISPS approach reconstructs the interference-plus-noise covariance matrix based on a simplified maximum entropy power spectral density function. The proposed CMR-ISPS beamformer can suppress interferers close to the direction of the signal of interest by producing notches in the directional response of the array with sufficient depths.
  arXiv  Detail & Related papers  (2023-09-02T23:58:01Z)
• Adaptive Annealed Importance Sampling with Constant Rate Progress [68.8204255655161]
  Annealed Importance Sampling (AIS) synthesizes weighted samples from an intractable distribution. We propose the Constant Rate AIS algorithm and its efficient implementation for $alpha$-divergences.
  arXiv  Detail & Related papers  (2023-06-27T08:15:28Z)
• Study of Robust Adaptive Beamforming with Covariance Matrix Reconstruction Based on Power Spectral Estimation and Uncertainty Region [0.0]
  A robust adaptive beamforming technique is proposed for uniform linear arrays. Two algorithms are presented to find the angular sector of interference in every snapshot. A power spectrum is introduced based on the estimation of the power of interference and noise components.
  arXiv  Detail & Related papers  (2023-03-18T23:09:40Z)
• Optimal Algorithms for the Inhomogeneous Spiked Wigner Model [89.1371983413931]
  We derive an approximate message-passing algorithm (AMP) for the inhomogeneous problem. We identify in particular the existence of a statistical-to-computational gap where known algorithms require a signal-to-noise ratio bigger than the information-theoretic threshold to perform better than random.
  arXiv  Detail & Related papers  (2023-02-13T19:57:17Z)
• Lattice partition recovery with dyadic CART [79.96359947166592]
  We study piece-wise constant signals corrupted by additive Gaussian noise over a $d$-dimensional lattice. Data of this form naturally arise in a host of applications, and the tasks of signal detection or testing, de-noising and estimation have been studied extensively in the statistical and signal processing literature. In this paper we consider instead the problem of partition recovery, i.e.of estimating the partition of the lattice induced by the constancy regions of the unknown signal. We prove that a DCART-based procedure consistently estimates the underlying partition at a rate of order $sigma2 k*
  arXiv  Detail & Related papers  (2021-05-27T23:41:01Z)
• Matrix completion based on Gaussian belief propagation [5.685589351789462]
  We develop a message-passing algorithm for noisy matrix completion problems based on matrix factorization. We derive a memory-friendly version of the proposed algorithm by applying a perturbation treatment commonly used in the literature of approximate message passing. Experiments on synthetic datasets show that while the proposed algorithm quantitatively exhibits almost the same performance under settings where the earlier algorithm is optimal, it is advantageous when the observed datasets are corrupted by non-Gaussian noise.
  arXiv  Detail & Related papers  (2021-05-01T12:16:49Z)
• Plug-And-Play Learned Gaussian-mixture Approximate Message Passing [71.74028918819046]
  We propose a plug-and-play compressed sensing (CS) recovery algorithm suitable for any i.i.d. source prior. Our algorithm builds upon Borgerding's learned AMP (LAMP), yet significantly improves it by adopting a universal denoising function within the algorithm. Numerical evaluation shows that the L-GM-AMP algorithm achieves state-of-the-art performance without any knowledge of the source prior.
  arXiv  Detail & Related papers  (2020-11-18T16:40:45Z)
• Optimization of loading factor preventing target cancellation [11.193504036335503]
  The paper presents an iterative algorithm for loading factor optimization based on sample of empirical signal to interference plus noise ratio (SINR) The proposed solution does not rely on any assumptions regarding the structure empirical covariance matrix and signal penetration model.
  arXiv  Detail & Related papers  (2020-10-09T14:04:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.