Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
Collaborative Direction of Arrival estimation by using Alternating Direction Method of Multipliers in distributed sensor array networks employing Sparse Bayesian Learning framework
Date
2022-10-01
Author
Nurbas, Ekin
Onat, Emrah
Tuncer, Temel Engin
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
216
views
0
downloads
Cite This
In this paper, we present a new method for Direction of Arrival (DoA) estimation in distributed sensor array networks by using Alternating Direction Method of Multipliers (ADMM) in Sparse Bayesian Learning (SBL) framework. Our proposed method, CDoAE, has certain advantages compared to previous distributed DoA estimation methods. It does not require any special array geometry and there is no need for inter -array frequency and phase matching. CDoAE uses the distributed ADMM to update the parameter set extracted by the SBL frameworks in the local arrays to minimize a common objective function. This update process is implemented in the master-node and the result is distributed back to the slave nodes. It is shown that the performance of the local arrays can be improved significantly in this distributed DOA estimation framework. Moreover, we present a method, CDoAE-TVR, to reduce the number of parameters transmitted from the local arrays to the master array which is important for the networks with limited bandwidth and energy. Several simulations have been performed including the cases with coherent sources. It is shown that the use of ADMM in a distributed fashion improves the SBL output efficiently and effectively. In addition, proposed CDoAE-TVR method reduces the transmitted parameters in the network with a small sacrifice on the DoA estimation performance.(c) 2022 Elsevier Inc. All rights reserved.
Subject Keywords
Direction of Arrival
,
Alternating Direction Method of Multipliers
,
Sparse Bayesian Learning
,
Distributed processing
,
DOA ESTIMATION
URI
https://hdl.handle.net/11511/100756
Journal
DIGITAL SIGNAL PROCESSING
DOI
https://doi.org/10.1016/j.dsp.2022.103739
Collections
Department of Electrical and Electronics Engineering, Article
Suggestions
OpenMETU
Core
Direction of Arrival estimation by using Alternating Direction Method of Multipliers in distributed sensor array networks
Nurbaş, Ekin; Tuncer, Temel Engin; Onat, Emrah; Department of Electrical and Electronics Engineering (2022-8-26)
In recent years, developments in microprocessor and wireless communication technologies have benefited a variety of distributed sensor network applications, including array signal processing. Researchers have been investigating distributed implementations of array signal processing algorithms, such as Direction of Arrival Estimation, for a variety of applications. Performance and practical implementations of those algorithms are affected by a variety of factors, such as inter-array phase and frequency match...
Fine resolution frequency estimation from three DFT samples: Case of windowed data
Candan, Çağatay (2015-09-01)
An efficient and low complexity frequency estimation method based on the discrete Fourier transform (DFT) samples is described. The suggested method can operate with an arbitrary window function in the absence or presence of zero-padding. The frequency estimation performance of the suggested method is shown to follow the Cramer-Rao bound closely without any error floor due to estimator bias, even at exceptionally high signal-to-noise-ratio (SNR) values.
Sparse linear sensor arrays: analysis of recent coarray based arrays and array design for nonlinear processing
Epçaçan, Erdal; Çiloğlu, Tolga; Department of Electrical and Electronics Engineering (2021-9-09)
In this thesis, sparse linear sensor arrays have been studied. The study of sparse arrays in this work can be considered under two main headings: Analysis and comparison of recently proposed coarray based arrays, and the adaptation of the nonlinear apodization method for linear arrays and its use in sparse linear array design. Recently proposed coarray-based sparse arrays are designed with closed form structures without the need for any optimization and have much higher degrees of freedom (DOF) than a unif...
Cooperative terrain based navigation and coverage identification using consensus
Kasebzadeh, Parinaz; Fritsche, Carsten; Özkan, Emre; Gunnarsson, Fredrik; Gustafsson, Fredrik ( Institute of Electrical and Electronics Engineers Inc.; 2015-07-06)
This paper presents a distributed online method for joint state and parameter estimation in a Jump Markov NonLinear System based on a distributed recursive Expectation Maximization algorithm. State inference is enabled via the use of Rao-Blackwellized Particle Filter and, for the parameter estimation, the E-step is performed independently at each sensor with the calculation of local sufficient statistics. An average consensus algorithm is used to diffuse local sufficient statistics to neighbors and approxim...
Direction of arrival estimation for nonuniform linear arrays by using array interpolation
Tuncer, Temel Engin; Friedlander, B. (2007-07-03)
[1] A new approach is proposed for DOA estimation in nonuniform linear arrays (NLA) based on array interpolation. A Wiener formulation is presented to improve the condition number of the mapping matrix as well as the performance for noisy observations. Noniterative and iterative methods for DOA estimation are proposed. These methods use an initial DOA which is then significantly improved by the subsequent processing. Partially augmentable nonredundant arrays (PANA) and partly filled NLA (PFNLA) are consider...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
E. Nurbas, E. Onat, and T. E. Tuncer, “Collaborative Direction of Arrival estimation by using Alternating Direction Method of Multipliers in distributed sensor array networks employing Sparse Bayesian Learning framework,”
DIGITAL SIGNAL PROCESSING
, vol. 130, pp. 0–0, 2022, Accessed: 00, 2022. [Online]. Available: https://hdl.handle.net/11511/100756.