Performance Evaluation &amp; Analysis of Direction of Arrival Estimation Algorithms using ULA

Raghu, K.; N, Prameela Kumari

doi:10.1109/iceeccot43722.2018.9001455

Cited by 7 publications

(2 citation statements)

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“…The w(n) are independent noise samples which is assumed to be zero-mean Gaussian random process with noise variance σ 2 . By Bayes's theorem [19], the posterior of unknown x by knowing the observed antenna array received signal y can be expresses as in (5). P(x/y)= P(y/x)P(x) P(y)…”

Section: Sparse Bayesian Learning Inferencementioning

confidence: 99%

“…In [4], an improved and modified MUSIC algorithm is proposed by employing matrix decomposition to address the case of coherent signal sources but the performance of this algorithm deteriorates for low SNR region. In [5,6], the performance of all these subspace based standard DOA estimation algorithms are analyzed and found that these techniques offer good speed and less complexity but suffer from low resolution, high sensitivity towards correlated signal sources and high MSE.…”

Section: Introductionmentioning

confidence: 99%

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Bayesian learning scheme for sparse DOA estimation based on maximum-a-posteriori of hyperparameters

Raghu

Kumari

2021

IJECE

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In this paper, the problem of direction of arrival estimation is addressed by employing Bayesian learning technique in sparse domain. This paper deals with the inference of sparse Bayesian learning (SBL) for both single measurement vector (SMV) and multiple measurement vector (MMV) and its applicability to estimate the arriving signal’s direction at the receiving antenna array; particularly considered to be a uniform linear array. We also derive the hyperparameter updating equations by maximizing the posterior of hyperparameters and exhibit the results for nonzero hyperprior scalars. The results presented in this paper, shows that the resolution and speed of the proposed algorithm is comparatively improved with almost zero failure rate and minimum mean square error of signal’s direction estimate.

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Section: Sparse Bayesian Learning Inferencementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%