Parameter estimation of railway vehicle dynamic model using rao-blackwellised particle filter

Li, Ping; Goodall, Roger M.; Kadirkamanathan, Visakan

doi:10.23919/ecc.2003.7085323

Cited by 38 publications

(29 citation statements)

References 9 publications

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“…If Model 4 is compared to Model 3, it can be seen that the matrices , and are independent of in Model 4, which implies that (28) This follows from (23b)-(23d) in Theorem 2.1. According to (28) only one instead of Riccati recursions is needed, which leads to a substantial reduction in computational complexity.…”

Section: B Important Model Classmentioning

confidence: 61%

“…According to (28) only one instead of Riccati recursions is needed, which leads to a substantial reduction in computational complexity. This is, of course, very important in real-time implementations.…”

Section: B Important Model Classmentioning

confidence: 99%

“…Models of this type are common and important in engineering applications, e.g., positioning, target tracking and collision avoidance [4], [18]. The marginalized particle filter has been successfully used in several applications, for instance, in aircraft navigation [32], underwater navigation [24], communications [9], [37], nonlinear system identification [28], [37], and audio source separation [3].…”

Section: Introductionmentioning

confidence: 99%

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Marginalized particle filters for mixed linear/nonlinear state-space models

Schön

Gustafsson

Nordlund

2005

IEEE Trans. Signal Process.

542

507

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Abstract-The particle filter offers a general numerical tool to approximate the posterior density function for the state in nonlinear and non-Gaussian filtering problems. While the particle filter is fairly easy to implement and tune, its main drawback is that it is quite computer intensive, with the computational complexity increasing quickly with the state dimension. One remedy to this problem is to marginalize out the states appearing linearly in the dynamics. The result is that one Kalman filter is associated with each particle. The main contribution in this paper is the derivation of the details for the marginalized particle filter for a general nonlinear state-space model. Several important special cases occurring in typical signal processing applications will also be discussed. The marginalized particle filter is applied to an integrated navigation system for aircraft. It is demonstrated that the complete high-dimensional system can be based on a particle filter using marginalization for all but three states. Excellent performance on real flight data is reported.

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Section: B Important Model Classmentioning

confidence: 61%

Section: B Important Model Classmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Marginalized particle filters for mixed linear/nonlinear state-space models

Schön

Gustafsson

Nordlund

2005

IEEE Trans. Signal Process.

542

507

View full text Add to dashboard Cite

show abstract

“…In the initial stages of the work, a Kalman filter [27] was used to estimate a generic smooth continuous conicity parameter applied to linear equations (16) and (17). The state of the Kalman filter was augmented to include the conicity parameter so that it could be estimated, therefore making the problem non-linear and an extended Kalman filter was used.…”

Section: Conicity Estimation Through Kalman Filteringmentioning

confidence: 99%

Condition monitoring opportunities using vehicle-based sensors

Ward¹,

Weston²,

Stewart³

et al. 2010

Proceedings of the Institution of Mechanical Engineers, Part F:

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Recent increases in railway patronage worldwide have created pressure on rolling stock and railway infrastructure through the demand to improve the capacity and punctuality of the whole system, and this demand must also be balanced with reducing life-cycle costs. Condition monitoring is seen as a significant contributor in achieving this. The emphasis of this article is on the use of sensors mounted on rolling stock to monitor the condition of infrastructure and the rolling stock itself. This is set in the context of modern rolling stock being fitted with high-capacity communication buses and multiple sensors, resulting in the potential for advanced processing of collected data. This article brings together linked research that uses a similar set of rolling stock sensors, and discusses: general usage and benefits, a track defect detection method, running gear condition monitoring, and absolute train speed detection.

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“…Initially Li et al (2003), Li et al (2004) and Li et al (2006) showed that condition monitoring of suspension components such as secondary dampers can be achieved in real time using RaoBlackwellised Particle Filters (RBPF). This was less successful at monitoring the condition of wear of the wheelset in combination with the rail, known as a linearised effective conicity function, λ.…”

Section: Introductionmentioning

confidence: 99%

Wheel-Rail Profile Condition Monitoring

Ward

Goodall

Dixon

2010

UKACC International Conference on CONTROL 2010

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Increased railway patronage worldwide is putting pressure on rolling stock and infrastructure to operate at higher capacity and with improved punctuality. Condition monitoring is seen as a contributing factor in enabling this and is highlighted here in the context of rolling stock being procured with high capacity data buses, multiple sensors and centralised control. This therefore leaves scope for advanced computational diagnostic concepts. The rail vehicle bogie and associated wheelsets are one of the largest and most costly areas of maintenance on rolling stock and presented here is a potential method for real time estimation of wheel-rail contact wear to move this currently scheduled based assessment to condition based assessment. This technique utilises recursive 'grey box' least squares system identification, used in a piecewise linear manner, to capture the strongly discontinuous nonlinear nature of the wheel-rail geometry.

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Parameter estimation of railway vehicle dynamic model using rao-blackwellised particle filter

Cited by 38 publications

References 9 publications

Marginalized particle filters for mixed linear/nonlinear state-space models

Marginalized particle filters for mixed linear/nonlinear state-space models

Condition monitoring opportunities using vehicle-based sensors

Wheel-Rail Profile Condition Monitoring

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