Variational Bayesian Inference for DOA Estimation Under Impulsive Noise and Non-Uniform Noise

Guo, Kun; Zhang, Liang; Li, Yingsong; Zhou, Tian; Yin, Jingwei

doi:10.1109/taes.2023.3265949

Cited by 4 publications

(1 citation statement)

References 36 publications

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“…Variational Bayesian inference (VBI) can be seen as a Bayesian approximate method that deals with the SR problem by maximizing a lower bound of likelihood function [32][33][34]. Compared with SBL, VBI replaces the point estimation of parameters with approximate posterior distribution estimates, which gives it a fast convergence speed.…”

Section: Introductionmentioning

confidence: 99%

Fast Variational Bayesian Inference for Space-Time Adaptive Processing

Zhang,

Wang,

Wang

2023

Remote Sensing

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Space-time adaptive processing (STAP) approaches based on sparse Bayesian learning (SBL) have attracted much attention for the benefit of reducing the training samples requirement and accurately recovering sparse signals. However, it has the problem of a heavy computational burden and slow convergence speed. To improve the convergence speed, the variational Bayesian inference (VBI) is introduced to STAP in this paper. Moreover, to improve computing efficiency, a fast iterative algorithm is derived. By constructing a new atoms selection rule, the dimension of the matrix inverse problem can be substantially reduced. Experiments conducted on the simulated data and measured data verify that the proposed algorithm has excellent clutter suppression and target detection performance.

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Section: Introductionmentioning

confidence: 99%

Fast Variational Bayesian Inference for Space-Time Adaptive Processing

Zhang,

Wang,

Wang

2023

Remote Sensing

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2-D DOA Estimation of Coherent Signals Using the Dual-Size Spreading Array

Li,

Cai

2024

J. Phys.: Conf. Ser.

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We introduce a novel approach for high-precision estimation of two-dimensional (2-D) Direction-of-Arrival (DOA) under conditions of coherent interference. This study innovatively devises an antenna configuration, termed the Dual-Size Spreading Array, specifically tailored to estimate DOAs in such environments. The method involves integrating a subarray with the primary array to create a Dual-Size Extended Array structure. This integration allows for a transformation of the covariance matrix into a new form, where its rank solely depends on the number of impinging waves. Both the subarray and primary array must have a scale no less than the number of incoming wavefronts. This proposed technique not only amplifies the effective aperture but also significantly reduces the computational intricacy associated with DOA estimation procedures. The efficacy of our methodology has been substantiated through simulation results, which demonstrate that it outperforms the conventional spatial smoothing techniques. Moreover, we provide several practical guidelines designed to enhance estimation accuracy while concurrently minimizing computational demands. In summary, this research presents an advanced solution that effectively addresses challenges in 2D DOA estimation under coherent scenarios, achieving improved performance and computational efficiency compared to existing methods.

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DOA Estimation for RIS Assisted Multi-User Wireless Communication System with Squared-Sine Error Criterion and Lawson Norm Constraint

Yu,

Li,

et al. 2023

2023 IEEE 6th International Conference on Electronic Information and Communication Technology (ICEICT)

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Variational Bayesian Inference for DOA Estimation Under Impulsive Noise and Non-Uniform Noise

Cited by 4 publications

References 36 publications

Fast Variational Bayesian Inference for Space-Time Adaptive Processing

Fast Variational Bayesian Inference for Space-Time Adaptive Processing

2-D DOA Estimation of Coherent Signals Using the Dual-Size Spreading Array

DOA Estimation for RIS Assisted Multi-User Wireless Communication System with Squared-Sine Error Criterion and Lawson Norm Constraint

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