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Distributed kernel mean embedding Gaussian belief propagation for underwater multi-sensor multi-target passive tracking*
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Dengpeng YANG, Yunfei GUO, Yanbo XUE, Anke XUE, Yun CHEN. Distributed kernel mean embedding Gaussian belief propagation for underwater multi-sensor multi-target passive tracking*[J]. Frontiers of Information Technology & Electronic Engineering, 1998, -1(-1): .
@article{title="Distributed kernel mean embedding Gaussian belief propagation for underwater multi-sensor multi-target passive tracking*",
author="Dengpeng YANG, Yunfei GUO, Yanbo XUE, Anke XUE, Yun CHEN",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="-1",
number="-1",
pages="",
year="1998",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2500204"
}
%0 Journal Article
%T Distributed kernel mean embedding Gaussian belief propagation for underwater multi-sensor multi-target passive tracking*
%A Dengpeng YANG
%A Yunfei GUO
%A Yanbo XUE
%A Anke XUE
%A Yun CHEN
%J Journal of Zhejiang University SCIENCE C
%V -1
%N -1
%P
%@ 2095-9184
%D 1998
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2500204
TY - JOUR
T1 - Distributed kernel mean embedding Gaussian belief propagation for underwater multi-sensor multi-target passive tracking*
A1 - Dengpeng YANG
A1 - Yunfei GUO
A1 - Yanbo XUE
A1 - Anke XUE
A1 - Yun CHEN
J0 - Journal of Zhejiang University Science C
VL - -1
IS - -1
SP -
EP -
%@ 2095-9184
Y1 - 1998
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2500204
Abstract: To address the problem of underwater multi-sensor multi-target passive tracking in clutter, a distributed kernel mean embedding-based Gaussian belief propagation (DKME-GaBP) algorithm is proposed. First, the joint posterior probability density function (pdf) is established and factorized, and it is represented by the corresponding factor graph. Then, the GaBP algorithm is run on this factor graph to reduce the computational complexity of data association. The factor graph of GaBP consists of inner and outer loops. The inner loop is responsible for local track estimation and data association. The outer loop fuses information from different sensors. For the inner loop, the kernel mean embedding (KME) with a Gaussian kernel is designed to transform the strong nonlinear problem of local estimation into a linear problem in a high-dimensional reproducing kernel Hilbert space (RKHS). For the outer loop, a multi-sensor distributed fusion method based on KME is proposed to improve fusion accuracy by accounting for the distance among different pdfs in RKHS. The effectiveness and robustness of DKME-GaBP are validated in simulation.
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