Evaluation of Estimation Algorithms: Credibility Tests

Li, X. R.; Zhao, Zhanlue; Li, Xiaobai

doi:10.1109/tsmca.2011.2158095

Cited by 36 publications

(12 citation statements)

References 25 publications

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“…Using (18), (20), (21), (22), (25), A4 and A5, we get a recursive evaluation of the mean of overall errorē k , ∀k. Note that the covariance of the e (i) k can be written as…”

Section: Mse Analysismentioning

confidence: 99%

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A low-complexity interacting multiple model filter for maneuvering target tracking

Khalid

Abrar

2017

AEU - International Journal of Electronics and Communications

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“…Using (18), (20), (21), (22), (25), A4 and A5, we get a recursive evaluation of the mean of overall errorē k , ∀k. Note that the covariance of the e (i) k can be written as…”

Section: Mse Analysismentioning

confidence: 99%

“…A number of measures to evaluate the similarity (or difference) of the filter generated covariance and true error covariance has been discussed in[24,25], i.e., NEES, Log-NEES, NC, UIT etc., Their relative merits and demerits have been reported in[25].…”

mentioning

confidence: 99%

A low-complexity interacting multiple model filter for maneuvering target tracking

Khalid

Abrar

2017

AEU - International Journal of Electronics and Communications

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“…and the noncredibility index (NCI) defined as [44,45] 8 MSE of position and velocity estimates is computed for one direction only to be consistent with problem setup in [42]. It should be, however, noted that the estimation performance is assumed to be equivalent for both directions.…”

Section: A Static Example: Transformation Of a Random Variablementioning

confidence: 99%

Random-point-based filters: analysis and comparison in target tracking

Duník

Straka

Šimandl

et al. 2015

IEEE Trans. Aerosp. Electron. Syst.

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This paper compares state estimation techniques for nonlinear stochastic dynamic systems, which are important for target tracking. Recently, several methods for nonlinear state estimation have appeared utilizing various random-point-based approximations for global filters (e.g., particle filter and ensemble Kalman filter) and local filters (e.g., Monte-Carlo Kalman filter and stochastic integration filters). A special emphasis is placed on derivations, algorithms, and commonalities of these filters. All filters described are put into a common framework, and it is proved that within a single iteration, they provide asymptotically equivalent results. Additionally, some deterministic-point-based filters (e.g., unscented Kalman filter, cubature Kalman filter, and quadrature Kalman filter) are shown to be special cases of a random-point-based filter. The paper demonstrates and compares the filters in three examples, a random variable transformation, re-entry vehicle tracking, and bearings-only tracking. The results show that the stochastic integration filter provides better accuracy than the Monte-Carlo Kalman filter and the ensemble Kalman filter with lower computational costs.

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“…We may find the optimal r ∈ [ 1 3 1] such that g(z) = z|z| r−1 minimizes P by numerical methods. Note that P is the filter estimated MSE, of which the accuracy depends on the credibility of assumptions on prior information and the accuracy of the numerical approximations [17].…”

Section: Design Guidelinesmentioning

confidence: 99%

Generalized linear minimum mean-square error estimation with application to space-object tracking

Liu

Chen

2013

2013 Asilomar Conference on Signals, Systems and Computers

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The linear minimum mean-square error (LMMSE) estimation has been shown to provide a good tradeoff between the computational requirement and estimation accuracy in nonlinear point estimation. However, the best estimator within the linear class may not be adequate to provide acceptable accuracy when dealing with a highly nonlinear problem. A generalized LMMSE (GLMMSE) estimation framework searches for the best estimator among all the estimators that are linear in a vector-valued function (namely, measurement transform function) of data. The measurement transform function may convert or augment the original measurement model. In this work, general guidelines for designing the GLMMSE estimator are discussed based on a numerical example. With a properly designed measurement transform function, GLMMSE estimation should perform no worse than LMMSE estimation if the moments involved can be computed exactly. We apply the GLMMSE estimation to a spaceobject tracking problem and its performance is compared with the conventional LMMSE estimator.

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Evaluation of Estimation Algorithms: Credibility Tests

Cited by 36 publications

References 25 publications

A low-complexity interacting multiple model filter for maneuvering target tracking

A low-complexity interacting multiple model filter for maneuvering target tracking

Random-point-based filters: analysis and comparison in target tracking

Generalized linear minimum mean-square error estimation with application to space-object tracking

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