Results 81 to 90 of about 7,085,552 (171)

NESTED SPARSE BAYESIAN LEARNING FOR BLOCK-SPARSE SIGNALS WITH INTRA-BLOCK CORRELATION

, 2014
In this work, we address the recovery of block sparse vectors with intra-block correlation, i.e., the recovery of vectors in which the correlated nonzero entries are constrained to lie in a few clusters, from noisy underdetermined linear measurements ...
Prasad, Ranjitha, Murthy, Chandra R, Rao, Bhaskar D   +2 more
core  

Sparse Bayesian Learning for Acoustic Source Localization

, 2021
The localization of acoustic sources is a parameter estimation problem where the parameters of interest are the direction of arrivals (DOAs). The DOA estimation problem can be formulated as a sparse parameter estimation problem and solved using ...
Aditya Siripuram, Santosh Nannuru, Nannuru, Santosh, Siripuram, Aditya, Ruchi Pandey, Pandey, Ruchi   +5 more
core   +1 more source

A Two-Step Dual-Polarimetric Sparse Bayesian Learning D-TomoSAR Method for Urban Building Deformation Monitoring

Remote Sensing
SAR tomography (TomoSAR) is capable of separating scatterers at different elevations within the same range–azimuth resolution cell. Differential TomoSAR (D-TomoSAR) further incorporates temporal information, enabling joint estimation of scatterer ...
Leixin Zhang, Feng Zhao, Yunjia Wang, Jordi J. Mallorqui, Zhouhao Su, Peibin Wang   +5 more
doaj   +1 more source

True-angle sparse Bayesian learning for horizontal line arrays in a multipath shadow-zone environment [PDF]

JASA Express Letters
Horizontal line arrays may exhibit large bearing-estimation errors in deep-ocean shadow zones, particularly near the endfire direction, because the received field is dominated by vertical-plane multipath rather than a direct arrival.
Zhengchao Huang, Zhenglin Li, Peng Xiao
doaj   +1 more source

Simultaneous Bayesian Sparse Approximation With Structured Sparse Models [PDF]

, 2016
Sparse approximation is key to many signal processing, image processing, and machine learning applications. If multiple signals maintain some degree of dependency, for example, the support sets are statistically related, then it will generally be ...

core   +2 more sources

Gaussian Mixture Model Based Bayesian Learning for Sparse Channel Estimation in Orthogonal Time Frequency Space Modulated Systems

IEEE Open Journal of Vehicular Technology
A novel Gaussian mixture model (GMM)–aided sparse Bayesian learning (SBL) framework is proposed for channel state information (CSI) estimation in orthogonal time-frequency space (OTFS) modulated systems. The key attribute of the proposed algorithm
Surbhi Gehlot, Suraj Srivastava, Sandeep Kumar Yadav, Lajos Hanzo   +3 more
doaj   +1 more source

Sparse Bayesian Learning for Channel Estimation in Time-Varying Underwater Acoustic OFDM Communication

IEEE Access, 2018
In this paper, we study the sparse Bayesian learning (SBL) framework for channel estimation in underwater acoustic (UWA) orthogonal frequency-division multiplexing (OFDM) communication systems, which provides a desirable property of preventing structural
Gang Qiao, Qingjun Song, Lu Ma, Songzuo Liu, Zongxin Sun, Shuwei Gan   +5 more
doaj   +1 more source

Revisiting CHAMPAGNE: Sparse Bayesian Learning as Reweighted Sparse Coding

International audienceThis paper revisits the CHAMPAGNE algorithm within the Sparse Bayesian Learning (SBL) framework and establishes its connection to reweighted sparse coding.
Kowalski, Matthieu, Mokhtari, Samy, Sechet, Dylan, Torrésani, Bruno   +3 more
core   +1 more source

Joint Approximately Sparse Channel Estimation and Data Detection in OFDM Systems Using Sparse Bayesian Learning

, 2014
It is well known that the impulse response of a wide-band wireless channel is approximately sparse, in the sense that it has a small number of significant components relative to the channel delay spread.
Prasad, Ranjitha, Murthy, Chandra R, Rao, Bhaskar D   +2 more
core   +1 more source

Wideband beam domain sparse Bayesian learning passive focusing localisation algorithm

IET Radar, Sonar & Navigation
To address the challenges of large‐aperture sonar systems passive localisation, this paper proposes the application of sparse Bayesian learning (SBL) for passive target localisation in the wideband beam domain. The proposed algorithm aims to overcome the
Hao Wang, Hong Zhang, Qiming Ma, Shuanping Du   +3 more
doaj   +1 more source