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DOA Tracking Based on Unscented Transform Multi-Bernoulli Filter in Impulse Noise Environment

Aiming at the problem of multiple-source direction of arrival (DOA) tracking in impulse noise, this paper models the impulse noise by using the symmetric α stable (SαS) distribution, and proposes a DOA tracking algorithm based on the Unscented Transform Multi-target Multi-Bernoulli (UT-MeMBer) filte...

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Detalles Bibliográficos
Autores principales: Wu, Sun-yong, Zhao, Jun, Dong, Xu-dong, Xue, Qiu-tiao, Cai, Ru-hua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6767214/
https://www.ncbi.nlm.nih.gov/pubmed/31540538
http://dx.doi.org/10.3390/s19184031
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author Wu, Sun-yong
Zhao, Jun
Dong, Xu-dong
Xue, Qiu-tiao
Cai, Ru-hua
author_facet Wu, Sun-yong
Zhao, Jun
Dong, Xu-dong
Xue, Qiu-tiao
Cai, Ru-hua
author_sort Wu, Sun-yong
collection PubMed
description Aiming at the problem of multiple-source direction of arrival (DOA) tracking in impulse noise, this paper models the impulse noise by using the symmetric α stable (SαS) distribution, and proposes a DOA tracking algorithm based on the Unscented Transform Multi-target Multi-Bernoulli (UT-MeMBer) filter framework. In order to overcome the problem of particle decay in particle filtering, UT is adopted to select a group of sigma points with different weights to make them close to the posterior probability density of the state. Since the α stable distribution does not have finite covariance, the Fractional Lower Order Moment (FLOM) matrix of the received array data is employed to replace the covariance matrix to formulate a MUSIC spatial spectra in the MeMBer filter. Further exponential weighting is used to enhance the weight of particles at high likelihood area and obtain a better resampling. Compared with the PASTD algorithm and the MeMBer DOA filter algorithm, the simulation results show that the proposed algorithm can more effectively solve the issue that the DOA and number of target are time-varying. In addition, we present the Sequential Monte Carlo (SMC) implementation of the UT-MeMBer algorithm.
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spelling pubmed-67672142019-10-02 DOA Tracking Based on Unscented Transform Multi-Bernoulli Filter in Impulse Noise Environment Wu, Sun-yong Zhao, Jun Dong, Xu-dong Xue, Qiu-tiao Cai, Ru-hua Sensors (Basel) Article Aiming at the problem of multiple-source direction of arrival (DOA) tracking in impulse noise, this paper models the impulse noise by using the symmetric α stable (SαS) distribution, and proposes a DOA tracking algorithm based on the Unscented Transform Multi-target Multi-Bernoulli (UT-MeMBer) filter framework. In order to overcome the problem of particle decay in particle filtering, UT is adopted to select a group of sigma points with different weights to make them close to the posterior probability density of the state. Since the α stable distribution does not have finite covariance, the Fractional Lower Order Moment (FLOM) matrix of the received array data is employed to replace the covariance matrix to formulate a MUSIC spatial spectra in the MeMBer filter. Further exponential weighting is used to enhance the weight of particles at high likelihood area and obtain a better resampling. Compared with the PASTD algorithm and the MeMBer DOA filter algorithm, the simulation results show that the proposed algorithm can more effectively solve the issue that the DOA and number of target are time-varying. In addition, we present the Sequential Monte Carlo (SMC) implementation of the UT-MeMBer algorithm. MDPI 2019-09-18 /pmc/articles/PMC6767214/ /pubmed/31540538 http://dx.doi.org/10.3390/s19184031 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Wu, Sun-yong
Zhao, Jun
Dong, Xu-dong
Xue, Qiu-tiao
Cai, Ru-hua
DOA Tracking Based on Unscented Transform Multi-Bernoulli Filter in Impulse Noise Environment
title DOA Tracking Based on Unscented Transform Multi-Bernoulli Filter in Impulse Noise Environment
title_full DOA Tracking Based on Unscented Transform Multi-Bernoulli Filter in Impulse Noise Environment
title_fullStr DOA Tracking Based on Unscented Transform Multi-Bernoulli Filter in Impulse Noise Environment
title_full_unstemmed DOA Tracking Based on Unscented Transform Multi-Bernoulli Filter in Impulse Noise Environment
title_short DOA Tracking Based on Unscented Transform Multi-Bernoulli Filter in Impulse Noise Environment
title_sort doa tracking based on unscented transform multi-bernoulli filter in impulse noise environment
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6767214/
https://www.ncbi.nlm.nih.gov/pubmed/31540538
http://dx.doi.org/10.3390/s19184031
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