Robust tracking method for multiple frequency-hopping signals

Jia Dong Shang; Zu Lin Wang; Li Na Zhou; Lan Yang

doi:10.13229/j.cnki.jdxbgxb201506046

Robust tracking method for multiple frequency-hopping signals

Jia Dong Shang
, Zu Lin Wang^*
, Li Na Zhou
, Lan Yang

^*Corresponding author for this work

School of Electronic and Information Engineering

Beihang University

Research output: Contribution to journal › Article › peer-review

Abstract

A robust tracking method for multiple frequency-hopping signals is presented. First, the measurement noise is modeled using the t distribution. Then, a novel Bayesian probability model is proposed to describe the multiple Frequency-Hopping (FH) signals tracking problem, such that the influence of the impulsive noise on the tracking performance can be attenuated efficiently. Finally, a Multiple Variational Sparse Bayesian Learning (M-VSBL) algorithm is derived for the proposed Bayesian model to estimate the hopping frequencies, directions of arrival and detect the hopping times. Simulation results demonstrate the robustness and accuracy of the proposed method under impulsive noise.

Original language	English
Pages (from-to)	2056-2061
Number of pages	6
Journal	Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition)
Volume	45
Issue number	6
DOIs	https://doi.org/10.13229/j.cnki.jdxbgxb201506046
State	Published - 1 Nov 2015

Keywords

Frequency-hopping signals tracking
Impulsive noise
Information processing
T distribution
Variational Bayesian learning

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10.13229/j.cnki.jdxbgxb201506046

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title = "Robust tracking method for multiple frequency-hopping signals",

abstract = "A robust tracking method for multiple frequency-hopping signals is presented. First, the measurement noise is modeled using the t distribution. Then, a novel Bayesian probability model is proposed to describe the multiple Frequency-Hopping (FH) signals tracking problem, such that the influence of the impulsive noise on the tracking performance can be attenuated efficiently. Finally, a Multiple Variational Sparse Bayesian Learning (M-VSBL) algorithm is derived for the proposed Bayesian model to estimate the hopping frequencies, directions of arrival and detect the hopping times. Simulation results demonstrate the robustness and accuracy of the proposed method under impulsive noise.",

keywords = "Frequency-hopping signals tracking, Impulsive noise, Information processing, T distribution, Variational Bayesian learning",

author = "Shang, \{Jia Dong\} and Wang, \{Zu Lin\} and Zhou, \{Li Na\} and Lan Yang",

year = "2015",

month = nov,

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doi = "10.13229/j.cnki.jdxbgxb201506046",

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volume = "45",

pages = "2056--2061",

journal = "Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition)",

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T1 - Robust tracking method for multiple frequency-hopping signals

AU - Shang, Jia Dong

AU - Wang, Zu Lin

AU - Zhou, Li Na

AU - Yang, Lan

PY - 2015/11/1

Y1 - 2015/11/1

N2 - A robust tracking method for multiple frequency-hopping signals is presented. First, the measurement noise is modeled using the t distribution. Then, a novel Bayesian probability model is proposed to describe the multiple Frequency-Hopping (FH) signals tracking problem, such that the influence of the impulsive noise on the tracking performance can be attenuated efficiently. Finally, a Multiple Variational Sparse Bayesian Learning (M-VSBL) algorithm is derived for the proposed Bayesian model to estimate the hopping frequencies, directions of arrival and detect the hopping times. Simulation results demonstrate the robustness and accuracy of the proposed method under impulsive noise.

AB - A robust tracking method for multiple frequency-hopping signals is presented. First, the measurement noise is modeled using the t distribution. Then, a novel Bayesian probability model is proposed to describe the multiple Frequency-Hopping (FH) signals tracking problem, such that the influence of the impulsive noise on the tracking performance can be attenuated efficiently. Finally, a Multiple Variational Sparse Bayesian Learning (M-VSBL) algorithm is derived for the proposed Bayesian model to estimate the hopping frequencies, directions of arrival and detect the hopping times. Simulation results demonstrate the robustness and accuracy of the proposed method under impulsive noise.

KW - Frequency-hopping signals tracking

KW - Impulsive noise

KW - Information processing

KW - T distribution

KW - Variational Bayesian learning

UR - https://www.scopus.com/pages/publications/84946408141

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DO - 10.13229/j.cnki.jdxbgxb201506046

M3 - 文章

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SN - 1671-5497

VL - 45

SP - 2056

EP - 2061

JO - Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition)

JF - Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition)

IS - 6

ER -

Robust tracking method for multiple frequency-hopping signals

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Keywords

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