An improved multiple model particle filtering approach for manoeuvring target tracking using Airborne GMTI with geographic information

Yu, Miao and Oh, Hyondong and Chen, Wen-Hua (2016) An improved multiple model particle filtering approach for manoeuvring target tracking using Airborne GMTI with geographic information. Aerospace Science and Technology, 52 . pp. 62-69. ISSN 1270-9638

Full content URL: http://dx.doi.org/10.1016/j.ast.2016.02.016

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An improved multiple model particle filtering approach for manoeuvring target tracking using Airborne GMTI with geographic information.pdf
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Abstract

This paper proposes a novel ground vehicle tracking method using an airborne ground moving target
indicator radar where the surrounding geographic information is considered to determine vehicle’s
movement type as well as constrain its positions. Multiple state models corresponding to different
movement modes are applied to represent the vehicle’s behaviour within different terrain conditions.
Based on geographic conditions and multiple state models, a constrained variable structure multiple
model particle filter algorithm aided by particle swarm optimisation is proposed. Compared with
the traditional multiple model particle filtering schemes, the proposed algorithm utilises a particle
swarm optimisation technique for the particle filter which generates more effective particles and
generated particles are constrained into the feasible geographic region. Numerical simulation results
in a realistic environment show that the proposed method achieves better tracking performance
compared with current state-of-the-art ones for manoeuvring vehicle tracking.

Additional Information:Open Access funded by Engineering and Physical Sciences Research Council
Keywords:Manoeuvring ground vehicle tracking, geographic information, variable structure, multiple models, particle ?lter, particle swarm optimisation, JCOpen
Subjects:G Mathematical and Computer Sciences > G720 Knowledge Representation
G Mathematical and Computer Sciences > G340 Statistical Modelling
G Mathematical and Computer Sciences > G120 Applied Mathematics
Divisions:College of Science > School of Computer Science
ID Code:26783
Deposited On:22 Mar 2017 16:11

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