Ellipsoidal set based robust particle filtering for recursive Bayesian state estimation

Shao, Xinguang; Zhao, Zhonggai; Liu, Fei; Huang, Biao

doi:10.1109/icca.2013.6564932

Cited by 3 publications

(7 citation statements)

References 11 publications

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“…In references related to the particle filtering, the most commonly used quality indicators are MSE

{normalM normalS normalE}_{j} = \frac{1}{M} true {true\sum}_{k = 1}^{M} {({\overset{x}{true}}_{j}^{(k)} - x_{j}^{(k) +})}^{2}

and RMSE

{RMSE}_{j} = \sqrt{{M S E}_{j}}

for presentation of results for one state variable, and average RMSE (aRMSE)

aRMSE = \frac{1}{2 B - 1} true {true\sum}_{j = 1}^{2 B - 1} {RMSE}_{j}

is used to show general results for objects with few state variables (2 B − 1 is the number of the state variables in the power system). The latter has been used as estimation quality index in this paper.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…In references related to the particle filtering, the most commonly used quality indicators are MSE [4,17]…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Additionally, it should be noted that both the transition model (3) and the measurement model (4) have been assumed to be Gaussian PDFs. For other models, the estimation quality of UKF method should be noticeably worse.…”

Section: Discussionmentioning

confidence: 99%

“…It should be also noted that if specified threshold will be too high, the algorithm may loop. In the combination of the set‐membership filter and the particle filter has been proposed. In this approach in each time step the bounding ellipsoid is calculated, and then particles drawn from transition model are compared with this ellipsoid—if a particle does not pass the boundary check (is located outside the ellipsoid), it should be redrawn.…”

Section: Introductionmentioning

confidence: 99%

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Robust particle filter-anti-zero bias modification

Kozierski

Lis

Horla

2016

Int. J. Robust. Nonlinear Control

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Summary A modification of the particle filter algorithm that allows using it also in cases with incorrect measurements has been presented in the paper. The use of anti‐zero bias does not require a large computational effort (a single additional operation for each measurement value), and simultaneously does not deteriorate results for the case of good measurements (if the bias value is not too large). It has been shown that the bias which provides the best estimation quality depends on the particles number. The obtained results have been compared with unscented Kalman filter method with bad measurement data identification. As an object, power system has been used, with main task set as estimation of the state of this system. Copyright © 2016 John Wiley & Sons, Ltd.

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“…In references related to the particle filtering, the most commonly used quality indicators are MSE

{normalM normalS normalE}_{j} = \frac{1}{M} true {true\sum}_{k = 1}^{M} {({\overset{x}{true}}_{j}^{(k)} - x_{j}^{(k) +})}^{2}

and RMSE

{RMSE}_{j} = \sqrt{{M S E}_{j}}

for presentation of results for one state variable, and average RMSE (aRMSE)

aRMSE = \frac{1}{2 B - 1} true {true\sum}_{j = 1}^{2 B - 1} {RMSE}_{j}

Section: Simulation Resultsmentioning

confidence: 99%

“…In references related to the particle filtering, the most commonly used quality indicators are MSE [4,17]…”

Section: Simulation Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Robust particle filter-anti-zero bias modification

Kozierski

Lis

Horla

2016

Int. J. Robust. Nonlinear Control

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“…A numerical example is presented in this section to compare the performance of the MMAE, the EKF, and the robust‐filtering algorithms for space surveillance. There have been a number of robust filters proposed in literature, such as the nonlinear robust filter (NRF) , the robust particle filter , and the robust moving horizon estimation . For clarity, a brief description of the NRF algorithm in is given in Appendix.…”

Section: Numerical Simulationsmentioning

confidence: 99%

Multiple-model adaptive estimation for space surveillance with measurement uncertainty

Xiong¹,

Wei²,

Liu³

2015

Optim. Control Appl. Meth.

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SUMMARYAn efficient multiple-model adaptive estimation (MMAE) algorithm is presented for time-variant system with both system and measurement uncertainties, whose statistics are supposed to be unknown. The model uncertainties are described by a set of noise covariance matrices, such that a small model set is sufficient to achieve good estimation performance. To demonstrate the feasibility of the presented MMAE for the considered time-variant uncertain system, a proof is provided that shows the filtering convergence. The performance of the algorithm is evaluated via different operating scenarios of double line-of-sight measuring space surveillance. Simulation results demonstrate that the MMAE algorithm outperforms the robust filtering algorithms in the presence of the uncertainty and yields positioning accuracy similar to the extended Kalman filter in the absence of the uncertainty.

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