SoftConPF TAES Code.zip (116.24 kB)
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Particle Filtering with Soft State Constraints (Matlab code)

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This zip file include the example Matlab code for the soft-constrained (auxiliary) particle filters.

The associated article has abstract:
In practice, additional knowledge about the target to be tracked, other than its fundamental dynamics, can often be modelled as a set of soft constraints and utilised in a filtering process to improve the tracking performance. This paper develops a general approach to the modelling of soft inequality constraints, and investigates particle filtering with soft state constraints for target tracking. We develop two particle filtering algorithms with soft inequality constraints, i.e. a sequential-importanceresampling particle filter and an auxiliary sampling mechanism. The latter probabilistically selects the candidate particles from the soft inequality constraints of the state variables so that they are more likely to comply with the soft constraints. The performances of the proposed algorithms are evaluated using Monte Carlo simulations in a target tracking scenario.

Funding

EPSRC: EP/J011525/1

EPSRC: EP/K014307/2

Towards More Autonomy for Unmanned Vehicles: Situational Awareness and Decision Making under Uncertainty

Engineering and Physical Sciences Research Council

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Engineering and Physical Sciences Research Council

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History

School

Aeronautical, Automotive, Chemical and Materials Engineering

Department

Aeronautical and Automotive Engineering

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