Gaussian mixture sigma-point particle filters for sequential probabilistic inference in dynamic state-space models

Merwe, Rudolph van der; Wan, Eric A.

doi:10.1109/icassp.2003.1201778

Cited by 416 publications

(803 citation statements)

References 4 publications

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“…Now we will present a brief overview of the main characteristics of the Sigma-Point Kalman Filters. See (van der Merwe & Wan, 2003) for more details.…”

Section: Sigma-point Kalman Filtersmentioning

confidence: 99%

“…The Sigma-Point Kalman Filters (SPKF) (van der Merwe & Wan, 2003), a family of filters based on derivativeless statistical linearization, achieve higher performance than EKF in many problems and are applicable to areas where EKFs can not be used.…”

Section: Sigma-point Kalman Filtersmentioning

confidence: 99%

“…Studies (Ito & Xiong, 2000) have shown that in practice, just as UKF, the CDKF generates estimates that are clearly superior to those calculated by an EKF. Merwe & Wan, 2001) are numerically efficient square-root forms derived from UKF and CDKF respectively. Instead of calculating the matrix square-root of the state covariance at each time step (a very costly operation) in order to buid the sigmapoint set, these forms propagate and update the squareroot of the state covariance directly in Cholesky factored form, using linear algebra techniques.…”

Section: The Central Difference Kalman Filtermentioning

confidence: 99%

“…It was shown that Sigma-Point Kalman Filters achieve very good performance when applied to time series (van der Merwe & Wan, 2003).…”

Section: Introductionmentioning

confidence: 99%

“…The Sigma-Point Kalman Filters (SPKF) (van der Merwe & Wan, 2003) is a family of filters based on derivativeless statistical linearization. It was shown that Sigma-Point Kalman Filters achieve very good performance when applied to time series (van der Merwe & Wan, 2003).…”

Section: Introductionmentioning

confidence: 99%

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Applying REC Analysis to Ensembles of Sigma-Point Kalman Filters

Pina

Zaverucha

2006

Artificial Neural Networks – ICANN 2006

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The Sigma-Point Kalman Filters (SPKF) is a family of filters that achieve very good performance when applied to time series. Currently most researches involving time series forecasting use the Sigma-Point Kalman Filters, however they do not use an ensemble of them, which could achieve a better performance. The REC analysis is a powerful technique for visualization and comparison of regression models. The objective of this work is to advocate the use of REC curves in order to compare the SPKF and ensembles of them and select the best model to be used.

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“…Now we will present a brief overview of the main characteristics of the Sigma-Point Kalman Filters. See (van der Merwe & Wan, 2003) for more details.…”

Section: Sigma-point Kalman Filtersmentioning

confidence: 99%

Section: Sigma-point Kalman Filtersmentioning

confidence: 99%