Multi-Sensor Tracking of a Maneuvering Target Using Multiple-Model Bernoulli Filter
Summary :
A novel approach for the multiple-model multi-sensor Bernoulli filter (MM-MSBF) based on the theory of finite set statistics (FISST) is proposed for a single maneuvering target tracking in the presence of detection uncertainty and clutter. First, the FISST is used to derive the multi-sensor likelihood function of MSBF, and then combining the MSBF filter with the interacting multiple models (IMM) algorithm to track the maneuvering target. Moreover, the sequential Monte Carlo (SMC) method is used to implement the MM-MSBF algorithm. Eventually, the simulation results are provided to demonstrate the effectiveness of the proposed filter.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E98-A No.12 pp.2633-2641
- Publication Date
- 2015/12/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.E98.A.2633
- Type of Manuscript
- PAPER
- Category
- Digital Signal Processing
Authors
Yong QIN
Huazhong University of Science and Technology
Hong MA
Huazhong University of Science and Technology
Li CHENG
Wuhan Institute of Technology
Xueqin ZHOU
Huazhong University of Science and Technology
Keyword
Latest Issue
Copyrights notice of machine-translated contents
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Cite this
Copy
Yong QIN, Hong MA, Li CHENG, Xueqin ZHOU, "Multi-Sensor Tracking of a Maneuvering Target Using Multiple-Model Bernoulli Filter" in IEICE TRANSACTIONS on Fundamentals,
vol. E98-A, no. 12, pp. 2633-2641, December 2015, doi: 10.1587/transfun.E98.A.2633.
Abstract: A novel approach for the multiple-model multi-sensor Bernoulli filter (MM-MSBF) based on the theory of finite set statistics (FISST) is proposed for a single maneuvering target tracking in the presence of detection uncertainty and clutter. First, the FISST is used to derive the multi-sensor likelihood function of MSBF, and then combining the MSBF filter with the interacting multiple models (IMM) algorithm to track the maneuvering target. Moreover, the sequential Monte Carlo (SMC) method is used to implement the MM-MSBF algorithm. Eventually, the simulation results are provided to demonstrate the effectiveness of the proposed filter.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E98.A.2633/_p
Copy
@ARTICLE{e98-a_12_2633,
author={Yong QIN, Hong MA, Li CHENG, Xueqin ZHOU, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Multi-Sensor Tracking of a Maneuvering Target Using Multiple-Model Bernoulli Filter},
year={2015},
volume={E98-A},
number={12},
pages={2633-2641},
abstract={A novel approach for the multiple-model multi-sensor Bernoulli filter (MM-MSBF) based on the theory of finite set statistics (FISST) is proposed for a single maneuvering target tracking in the presence of detection uncertainty and clutter. First, the FISST is used to derive the multi-sensor likelihood function of MSBF, and then combining the MSBF filter with the interacting multiple models (IMM) algorithm to track the maneuvering target. Moreover, the sequential Monte Carlo (SMC) method is used to implement the MM-MSBF algorithm. Eventually, the simulation results are provided to demonstrate the effectiveness of the proposed filter.},
keywords={},
doi={10.1587/transfun.E98.A.2633},
ISSN={1745-1337},
month={December},}
Copy
TY - JOUR
TI - Multi-Sensor Tracking of a Maneuvering Target Using Multiple-Model Bernoulli Filter
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 2633
EP - 2641
AU - Yong QIN
AU - Hong MA
AU - Li CHENG
AU - Xueqin ZHOU
PY - 2015
DO - 10.1587/transfun.E98.A.2633
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E98-A
IS - 12
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - December 2015
AB - A novel approach for the multiple-model multi-sensor Bernoulli filter (MM-MSBF) based on the theory of finite set statistics (FISST) is proposed for a single maneuvering target tracking in the presence of detection uncertainty and clutter. First, the FISST is used to derive the multi-sensor likelihood function of MSBF, and then combining the MSBF filter with the interacting multiple models (IMM) algorithm to track the maneuvering target. Moreover, the sequential Monte Carlo (SMC) method is used to implement the MM-MSBF algorithm. Eventually, the simulation results are provided to demonstrate the effectiveness of the proposed filter.
ER -
English
