Comments on “Finite-Horizon Robust Kalman Filtering for Uncertain Discrete Time-Varying Systems With Uncertain-Covariance White Noises”

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 standard UKF gain in (15) can be recovered by setting W i k = 0 in (20), and by doing so, the DUKF reduces to the standard UKF.…”

Section: Remarkmentioning

confidence: 99%

Sensitivity reduction of unscented Kalman filter about parameter uncertainties

Shen

Karlgaard

2015

IET Radar, Sonar & Navigation

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“…Remark 2. Compared with the literature [15,16], it is obvious that the designed robust Kalman filter does not apply to the case that the measurement delay appears in the system. Meanwhile, the definition of the uncertainty matrices ΔA and ΔC is different from the definition of the corresponding matrices in [15,16].…”

Section: Abstract and Applied Analysismentioning

confidence: 99%

“…Compared with the literature [15,16], it is obvious that the designed robust Kalman filter does not apply to the case that the measurement delay appears in the system. Meanwhile, the definition of the uncertainty matrices ΔA and ΔC is different from the definition of the corresponding matrices in [15,16]. So, in order to facilitate the robust filter design, we need to utilize the state augmentation method to obtain a new uncertain system in (27).…”

Section: Abstract and Applied Analysismentioning

confidence: 99%

“…To compensate the misalignment errors of sensor and star sensor measurement delays, we have designed a finite-horizon robust Kalman filter by finding the upper bound of the estimation error covariance and minimizing the upper bound. It is worth mentioning that, due to the presence of the star sensor measurement delays, the upper bound of the estimation error covariance in (35) and the filter parameters A and K in (48) and (49) distinguished our work from the counterpart in [15,16]. In addition, this paper talks about the filter problem with only one type of noise disturbance.…”

Section: Finite-horizon Robust Kalman Filter Designmentioning

confidence: 99%

“…Recently, the finite-horizon robust Kalman filter design has been investigated to be available for handling the filtering problem with model uncertainties in the state, output, and noise matrices by getting an optimized upper bound on the estimation error covariance [15]. Souto and Ishihara [16] extend this work by considering correlated noises with unknown mean and variance. However, the above works have been based on this assumption that all the observations should be available at the time of estimation.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Finite-Horizon Robust Kalman Filter for Uncertain Attitude Estimation System with Star Sensor Measurement Delays

Qian¹,

Huang²,

Liu³

2014

Abstract and Applied Analysis

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This paper addresses the robust Kalman filtering problem for uncertain attitude estimation system with star sensor measurement delays. Combined with the misalignment errors and scale factor errors of gyros in the process model and the misalignment errors of star sensors in the measurement model, the uncertain attitude estimation model can be established, which indicates that uncertainties not only appear in the state and output matrices but also affect the statistic of the process noise. Meanwhile, the phenomenon of star sensor measurement delays is described by introducing Bernoulli random variables with different delay characteristics. The aim of the addressed attitude estimation problem is to design a filter such that, in the presence of model uncertainties and star sensors delays for the attitude estimation system, the optimized filter parameters can be obtained to minimize the upper bound on the estimation error covariance. Therefore, a finite-horizon robust Kalman filter is proposed to cope with this question. Compared with traditional attitude estimation algorithms, the designed robust filter takes into account the effects of star sensor measurement delays and model uncertainties. Simulation results illustrate the effectiveness of the developed robust filter.

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Kalman smoother error bounds in the presence of misspecified measurements

Teichner,

Meir

2023

IFAC-PapersOnLine

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Comments on “Finite-Horizon Robust Kalman Filtering for Uncertain Discrete Time-Varying Systems With Uncertain-Covariance White Noises”

Cited by 13 publications

References 5 publications

Sensitivity reduction of unscented Kalman filter about parameter uncertainties

Sensitivity reduction of unscented Kalman filter about parameter uncertainties

Finite-Horizon Robust Kalman Filter for Uncertain Attitude Estimation System with Star Sensor Measurement Delays

Kalman smoother error bounds in the presence of misspecified measurements

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