Dual Extended Kalman Filter Methods

Wan, Eric A.; Nelson, A.

doi:10.1002/0471221546.ch5

Cited by 163 publications

(111 citation statements)

References 30 publications

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“…In this paper, we use another suboptimal algorithm, socalled dual estimation [10] [16]. The idea of dual estimation is to separate joint state and parameter estimation into two independent processes.…”

Section: Bayesian Filtering For Simultaneous Localization and Biamentioning

confidence: 99%

“…Different approximate estimators like the Extended Kalman Filter (EKF) [6], Unscented Kalman filter (UKF) [7], Particle Filter [8] or Hybrid Density Filter [9] could solve this problem. Additionally, dual estimation [10] or the expectationmaximization (EM) algorithm [11], which decouples the state and parameter estimation into two different problems in a suboptimal way, can also be used.…”

Section: Introductionmentioning

confidence: 99%

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Simultaneous multi-information fusion and parameter estimation for robust 3-D indoor positioning systems

Wang

Szabo

Bamberger

et al. 2008

2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems

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Abstract-Typical WLAN based indoor positioning systems take the received signal strength (RSS) as the major information source. Due to the complicated indoor environment, the RSS measurements are hard to model and too noisy to achieve a satisfactory 3-D accuracy in multi-floor scenarios. To enhance the performance of WLAN positioning systems, extra information sources could be integrated. In this paper, a Bayesian framework is applied to fuse multi-information sources and estimate the spatial and time varying parameters simultaneously and adaptively. An application of this framework, which fuses pressure measurements, a topological building map with RSS measurements, and simultaneously estimates the pressure sensor bias, is investigated. Our experiments indicate that the localization performance is more accurate and robust by using our approach.

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Section: Bayesian Filtering For Simultaneous Localization and Biamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Simultaneous multi-information fusion and parameter estimation for robust 3-D indoor positioning systems

Wang

Szabo

Bamberger

et al. 2008

2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems

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“…Hence, our paper contributes to the literature by developing a sophisticated nonlinear lter that is able to simultaneously infer the default risk and associated model parameters from the term structure of CDS spreads. This is especially useful and practical in the real world when variables need to be tracked in real time or a quick estimate of state variables is required (Kitagawa and Sato (2001), Liu and West (2001) and Wan and Nelson (2001)). …”

Section: Introductionmentioning

confidence: 99%

Recovering default risk from CDS spreads with a nonlinear filter

Guarin¹,

Liu²,

Ng³

2014

Journal of Economic Dynamics and Control

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We propose a nonlinear lter to estimate the time-varying instantaneous default risk from the term structure of credit default swap (CDS) spreads. Based on the numerical solution of the Fokker-Planck equation (FPE) using a meshfree interpolation method, the lter performs a joint estimation of default intensities and CIR model parameters. As the FPE can account for nonlinear functions and nonGaussian errors, the proposed framework provides more exibility and accuracy.We test the nonlinear lter on simulated CDS spreads and apply it to daily CDS spreads of the Dow Jones Industrial Average component companies from 2005 to 2010 with supportive results.

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“…Moreover, if strength degradation is taking place inside the system because of damage growth, results are further worsened and accurate model calibrations are hardly obtained [14,16]. Alternative, more sophisticated linearization schemes, like the one adopted by the dual EKF [17], can lead to better performances, but the additional computational costs often preclude the use of these filters for on-line identification [15].…”

Section: Introductionmentioning

confidence: 99%

Unscented Kalman filtering for nonlinear structural dynamics

2007

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Joint estimation of unknown model parameters and unobserved state components for stochastic, nonlinear dynamic systems is customarily pursued via the extended Kalman filter (EKF). However, in the presence of severe nonlinearities in the equations governing system evolution, the EKF can become unstable and accuracy of the estimates gets poor. To improve the results, in this paper we account for recent developments in the field of statistical linearization and propose an unscented Kalman filtering procedure. In the case of softening single degree-of-freedom structural systems, we show that the performance of the unscented Kalman filter (UKF), in terms of state tracking and model calibration, is significantly superior to that of the EKF.

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Dual Extended Kalman Filter Methods

Cited by 163 publications

References 30 publications

Simultaneous multi-information fusion and parameter estimation for robust 3-D indoor positioning systems

Simultaneous multi-information fusion and parameter estimation for robust 3-D indoor positioning systems

Recovering default risk from CDS spreads with a nonlinear filter

Unscented Kalman filtering for nonlinear structural dynamics

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