New environmental dependent modeling with Gaussian particle filtering based implementation for ground vehicle tracking
Miao Yu
Yali Xue
Runxiao Ding
Hyondong Oh
Wen-Hua Chen
Jonathon Chambers
2134/22671
https://repository.lboro.ac.uk/articles/conference_contribution/New_environmental_dependent_modeling_with_Gaussian_particle_filtering_based_implementation_for_ground_vehicle_tracking/9222428
This paper proposes a new domain knowledge aided Gaussian particle filtering based approach for the ground vehicle tracking application. Firstly, a new form of modeling is proposed to reflect the influences of different types of environmental domain knowledge on the vehicle dynamic: i) a non-Markov jump model is applied with multiple models while transition probabilities between models are environmental dependent ii) for a particular model, both the constraints and potential forces
obtained from the surrounding environment have been applied to refine the vehicle state distribution. Based on the proposed
modeling approach, a Gaussian particle filtering based method is developed to implement the related Bayesian inference for
the target state estimation. Simulation studies from multiple Monte Carlo simulations confirm the advantages of the proposed method over traditional ones, from both the modeling and implementation aspects.
2016-10-05 10:52:48
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Engineering not elsewhere classified