DOA Estimation in Heteroscedastic Noise with sparse Bayesian Learning

被引：0

作者：

Gerstoft, Peter ^{[1
]}

Mecklenbraeuker, Christoph F. ^{[2
]}

Nannuru, Santosh ^{[3
]}

Leus, Geert ^{[4
]}

机构：

[1] UCSD, NoiseLab, La Jolla, CA 92093 USA

[2] TU Wien, Inst Telecommun, Vienna, Austria

[3] IIIT Hyderabad, SPCRC, Hyderabad, India

[4] Delft Univ Technol, Dept Elect Engn, Delft, Netherlands

来源：

APPLIED COMPUTATIONAL ELECTROMAGNETICS SOCIETY JOURNAL | 2020年 / 35卷 / 11期

关键词：

Heteroscedastic noise; sparse reconstruction; LIKELIHOOD; MULTIPLE;

D O I：

10.47037/2020.ACES.J.351188

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

We consider direction of arrival (DOA) estimation from long-term observations in a noisy environment. In such an environment the noise source might evolve, causing the stationary models to fail. Therefore a heteroscedastic Gaussian noise model is introduced where the variance can vary across observations and sensors. The source amplitudes are assumed independent zero-mean complex Gaussian distributed with unknown variances (i.e., source powers), leading to stochastic maximum likelihood (ML) DOA estimation. The DOAs are estimated from multi-snapshot array data using sparse Bayesian learning (SBL) where the noise is estimated across both sensors and snapshots.

引用

收藏

页码：1439 / 1440

页数：2

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