Bayesian Estimation and Tracking

Haug, Anton J.

doi:10.1002/9781118287798

Cited by 146 publications

(111 citation statements)

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“…Therefore, a performance measure different from visual inspection should be considered. The performance measures for family of Kalman filters are provided by Haug [30]. In this study, root mean squared error is used as the performance measure.…”

Section: Unscented Kalman Filter and Estimation Resultsmentioning

confidence: 99%

“…Readers are referred to the studies by Kandepu et al [28] and Matzuka et al [29] which explain the application of UKF in nonlinear dynamic systems for state and parameter estimation. Additionally, a review of performance measures for such kind of filters are provided by Haug [30]. This estimation approach can be used to obtain a friction map of different rail sections or to alert driver.…”

Section: Introductionmentioning

confidence: 99%

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A new friction condition identification approach for wheel–rail interface

Onat

Voltr

Lata

2016

International Journal of Rail Transportation

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In recent years, there has been an increasing interest in designing intelligent vehicles such that they can take necessary actions according to the environmental changes around them and they can inform decision makers about these changes. For safer and cheaper transport, dynamic modelling of these vehicles and identification of such changes in environment based on these models plays an important role. In this study, a sigma point Kalman filter based scheme (i.e. joint unscented Kalman filter) is proposed to estimate maximum friction coefficient as a parameter in wheel-rail interface. This estimation scheme uses interpretation of lateral and yaw dynamic response of a wheelset to identify maximum friction coefficient. This joint unscented Kalman filter based approach provides information about the friction conditions in wheel-rail interface without post-processing of estimated data.

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Section: Unscented Kalman Filter and Estimation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A new friction condition identification approach for wheel–rail interface

Onat

Voltr

Lata

2016

International Journal of Rail Transportation

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“…Basic bootstrap particle filter was used [15]. The dualmode implementation always started with particle filter turned on since it is considered to be the more robust filter.…”

Section: B Particle Filtermentioning

confidence: 99%

Adaptive real-time dual-mode filter design for seamless pedestrian navigation

Peltola

Hill

Moore

2017

2017 International Conference on Localization and GNSS (ICL-GNSS)

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Abstract-Seamless navigation requires that the mobile device is capable of offering a position solution both indoors and outdoors. Novel seamless navigation system design was implemented and tested to achieve this aim. The design consists of general navigation system framework blocks and of the necessary interface agreements between the blocks. This approach enables plug-and-play style design of modules.The implementation used four preselected key technologies. Microstrain 3DM-GX4-45 foot-mounted inertial measurement unit sensor data was fused together with the u-blox GNSS receiver positions outdoors. Context sensitive inference engine enabled the fusion of position updates indoors from the Decawave TREK1000 Ultra WideBand ranging kit and from the 6 Kontakt.io/Raspberry Pi anchor-based Bluetooth low energy fingeprinting system.Novel dual-mode filter design uses a particle filter and the pentagon buffer enhanced Kalman filter in the position solution derivation. Depending on the map and the walls in the environment and on the quality of position updates, the implemented control logic employs the most fit filter for the current context. Computational power is now focussed, when particle filter is needed. The novel pentagon buffer enhanced Kalman filter is 10 times faster, allowing power saving when situation is not too critical. Moreover, the buffer provides position updates by interacting with the map and helps to correct the position solution.The navigation system is seamless according to the tests conducted around and within the Nottingham Geospatial building. No user input is needed for smooth transition from outdoors to indoors and vice versa. The system achieves an accuracy of 2.35 m outdoors and 1.4 m indoors (95% of error). Inertial system availability was continuous, while GNSS was available outdoors and BLE and UWB indoors.

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“…N s is the total number of particles. For Bootstrap Particle Filter (BPF) [11], which is commonly used and efficiently implementable, the associated weights can be calculated as:…”

Section: Particle Filtersmentioning

confidence: 99%

Fine-grained indoor tracking by fusing inertial sensor and physical layer information in WLANs

Acuna

Zhao

et al. 2016

2016 IEEE International Conference on Communications (ICC)

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Indoor positioning has become an emerging research area because of huge commercial demands for location-based services in indoor environments. Channel State Information (CSI) as a fine-grained physical layer information has been recently proposed to achieve high positioning accuracy by using range-based methods, e.g., trilateration. In this work, we propose to fuse the CSI-based ranges and velocity estimated from inertial sensors by an enhanced particle filter to achieve highly accurate tracking. The algorithm relies on some enhanced ranging methods and further mitigates the remaining ranging errors by a weighting technique. Additionally, we provide an efficient method to estimate the velocity based on inertial sensors. The algorithms are designed in a network-based system, which uses rather cheap commercial devices as anchor nodes. We evaluate our system in a complex environment along three different moving paths. Our proposed tracking method can achieve 1.3m for mean accuracy and 2.2m for 90% accuracy, which is more accurate and stable than pedestrian dead reckoning and range-based positioning.

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Bayesian Estimation and Tracking

Cited by 146 publications

References 0 publications

A new friction condition identification approach for wheel–rail interface

A new friction condition identification approach for wheel–rail interface

Adaptive real-time dual-mode filter design for seamless pedestrian navigation

Fine-grained indoor tracking by fusing inertial sensor and physical layer information in WLANs

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