Constrained Bayesian state estimation – A comparative study and a new particle filter based approach

Shao, Xinguang; Huang, Biao; Lee, Jong Min

doi:10.1016/j.jprocont.2009.11.002

Cited by 113 publications

(74 citation statements)

References 32 publications

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“…Moreover, to comply with the stall speed the forward speeds in those models need to satisfy the constraintsẋ k ≥ 20m/s,ẏ k ≥ 20m/s, respectively. These state constraints can be incorporated in the particle filter by using the constrained likelihood function [23]. The observation noise v k introduced in (7) is also assumed to be a Gaussian random variable which has zero mean and covariance matrix To demonstrate the target tracking performance on the proposed scenario, Monte Carlo simulations were carried out for L = 100 random generated tracks.…”

Section: Case Studymentioning

confidence: 99%

Enhanced situation awareness for unmanned aerial vehicle operating in terminal areas with circuit flight rules

Liu

Coombes

et al. 2016

Proceedings of the Institution of Mechanical Engineers, Part G:

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This paper considers the situation awareness function associated with an unmanned aerial vehicle arriving at an uncontrolled airfield. Given no air traffic control service available within such a terminal area, the unmanned aerial vehicle needs to establish a good level of situation awareness by using its onboard sensors to detect and track other traffic aircraft. Comparing to the existing works which mainly use sensor observations in the filtering process, this paper exploits the circuit flight rules to provide extra knowledge about the target behaviour. This is achieved by using multiple models to describe the target motions in different flight phases and characterising the phase transition in a stochastic manner. Consequently, an interacting multiple model particle filter with state-dependent transition probabilities is developed to provide the required situation awareness function.

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Section: Case Studymentioning

confidence: 99%

Enhanced situation awareness for unmanned aerial vehicle operating in terminal areas with circuit flight rules

Liu

Coombes

et al. 2016

Proceedings of the Institution of Mechanical Engineers, Part G:

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“…The nature of sample based representation of the PF facilitates incorporating constraints into the estimation procedure [43]. Gas path health parameters of turbofan engines vary within a certain range, therefore, the health parameter estimation is actually a state estimation problem with constraints.…”

Section: The Constrained Ekpfmentioning

confidence: 99%

Gas Path Health Monitoring for a Turbofan Engine Based on a Nonlinear Filtering Approach

Lü

Huang

2013

Energies

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Different approaches for gas path performance estimation of dynamic systems are commonly used, the most common being the variants of the Kalman filter. The extended Kalman filter (EKF) method is a popular approach for nonlinear systems which combines the traditional Kalman filtering and linearization techniques to effectively deal with weakly nonlinear and non-Gaussian problems. Its mathematical formulation is based on the assumption that the probability density function (PDF) of the state vector can be approximated to be Gaussian. Recent investigations have focused on the particle filter (PF) based on Monte Carlo sampling algorithms for tackling strong nonlinear and non-Gaussian models. Considering the aircraft engine is a complicated machine, operating under a harsh environment, and polluted by complex noises, the PF might be an available way to monitor gas path health for aircraft engines. Up to this point in time a number of Kalman filtering approaches have been used for aircraft turbofan engine gas path health estimation, but the particle filters have not been used for this purpose and a systematic comparison has not been published. This paper presents gas path health monitoring based on the PF and the constrained extend Kalman particle filter (cEKPF), and then compares the estimation accuracy and computational effort of these filters to the EKF for aircraft engine performance estimation under rapid faults and general deterioration. Finally, the effects of the constraint mechanism and particle number on the cEKPF are discussed. We show in this paper that the cEKPF outperforms the EKF, PF and EKPF, and conclude that the cEKPF is the best choice for turbofan engine health monitoring. OPEN ACCESSEnergies 2013, 6 493

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“…In the literature, there are only a few particle filters developed for dealing with state constraints, among which the pioneering research work in [6] and [7] is particularly worth noting. In [6], a simple algorithm using the acceptance-rejection method was developed.…”

Section: Introductionmentioning

confidence: 99%

“…However, it is in general not suitable for online applications because the time required for drawing a particle that satisfies the state constraints could be prohibitively long. To circumvent this problem, a novel approach was proposed in [7] that consists of two stages. First, particle candidates are drawn without using the state constraints; then the candidates that violate the constraints are projected into the feasible area defined by the constraints using a series of optimization.…”

Section: Introductionmentioning

confidence: 99%

An Auxiliary Particle Filtering Algorithm With Inequality Constraints

Liu

Chen

2017

IEEE Trans. Automat. Contr.

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Constrained Bayesian state estimation – A comparative study and a new particle filter based approach

Cited by 113 publications

References 32 publications

Enhanced situation awareness for unmanned aerial vehicle operating in terminal areas with circuit flight rules

Enhanced situation awareness for unmanned aerial vehicle operating in terminal areas with circuit flight rules

Gas Path Health Monitoring for a Turbofan Engine Based on a Nonlinear Filtering Approach

An Auxiliary Particle Filtering Algorithm With Inequality Constraints

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