The Kalman filter - Its recognition and development for aerospace applications

Schmidt, S. F.

doi:10.2514/3.19713

Cited by 132 publications

(57 citation statements)

References 4 publications

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“…Therefore, in state estimation problems, PF is often used as an alternative to the Extended Kalman Filter (EKF) [2] or the Unscented Kalman Filter (UKF) [3]. With infinite samples, PF can approach the Bayesian optimal estimate [4], so it is more accurate than the EKF or UKF.…”

Section: Introductionmentioning

confidence: 99%

A biologically inspired improvement strategy for particle filter: Ant colony optimization assisted particle filter

Zhong

Fung

Dai

2010

Int. J. Control Autom. Syst.

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Particle Filter (PF) is a sophisticated model estimation technique based on simulation. Due to the natural limitations of PF, two problems, namely particle impoverishment and sample size dependency, frequently occur during the particles updating stage and these problems will limit the accuracy of the estimation results. In order to alleviate these problems, Ant Colony Optimization is incorporated into the generic PF before the updating stage. After executing the Ant Colony optimization, impoverished particle samples will be re-positioned and closer to their locally highest likelihood distribution function. Our experimental results show that the proposed algorithm can realize better tracking performance when comparing to the generic PF, the Extended Kalman Filter and other enhanced versions of PF.

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Section: Introductionmentioning

confidence: 99%

A biologically inspired improvement strategy for particle filter: Ant colony optimization assisted particle filter

Zhong

Fung

Dai

2010

Int. J. Control Autom. Syst.

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“…One is the EKF (Extended Kalman Filter) algorithm, it ignores the high order term and linear approximates to nonlinear state [3], but its weakness is that the estimation accuracy is low and prone to loose, etc. One is the PF(Particle Filter) algorithm, PF is a kind of filter algorithm which based on Monte Carlo and recursive bayesian estimation.…”

Section: Introductionmentioning

confidence: 99%

An UKF target tracking method with effective suppression of angular glint

Zhang

2013

2013 10th IEEE International Conference on Control and Automation (ICCA)

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Angular glint noise is a kind of correlated and non-gaussian noise. When dealing with tracking problem in correlated and non-gaussian noise environment, the performance of traditional Unscented Kalman Filter(UKF) will degrade significantly. In order to solve the target tracking problem under the angular glint, this paper raises a new UKF algorithm based on the measurement equations linearization, and it is compared with traditional UKF. The simulation result shows that the new method can effectively get the nonlinear system state estimation in the colored noise environment. Because the linearization is proceeded during the process of solving the new measurement equations' error, so the accuracy of this method is influenced smaller, and this method has extensive adaptability.

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“…Such deviations can be in the form of model parameter uncertainty or uncertainties in the assumed process and measurement noise statistics, such as non-Gaussian errors, and improving the robustness of filters about these deviations has been the subject of extensive research in the past. Schmidt [7] presents two techniques to deal with the uncertainties, namely, a dual state-parameter estimation approach and the so-called Kalman-Schmidt filter [8] or the consider KF [9] where the parameters are considered as structured process and/or measurement noise. Similarly, Haddad and Bernstein [10] devise reduced-order robust linear filters where the parameter uncertainties are treated as state and measurement dependent random errors.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity reduction of unscented Kalman filter about parameter uncertainties

Shen

Karlgaard

2015

IET Radar, Sonar & Navigation

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This study aims to reduce the sensitivity of the unscented Kalman filter (UKF) estimates with respect to uncertain model parameters, leading to a more robust UKF. The standard minimum-variance cost index is augmented to include a penalty on the sensitivities of the state estimates about parameter uncertainties in the form of a weighted norm. A new filter gain is thus obtained, and the desensitised UKF (DUKF) provides better estimation of the true states than the standard UKF with an imperfect plant model. Numerical examples are shown to demonstrate the efficacy of the DUKF.

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The Kalman filter - Its recognition and development for aerospace applications

Cited by 132 publications

References 4 publications

A biologically inspired improvement strategy for particle filter: Ant colony optimization assisted particle filter

A biologically inspired improvement strategy for particle filter: Ant colony optimization assisted particle filter

An UKF target tracking method with effective suppression of angular glint

Sensitivity reduction of unscented Kalman filter about parameter uncertainties

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