Accuracy improvement by implementing sequential measurement update in robust Kalman filter

Chang, Guobin; Wang, Yunjia; Wang, Qianxin

doi:10.1007/s40328-015-0134-4

Cited by 9 publications

(5 citation statements)

References 24 publications

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“…The sequential KF decomposes the measurement update into N submeasurement updates, which can reduce the computational complexity of the matrix inversion. Correcting the measurement noise variance based on sequential filtering is beneficial for more targeted adaptation to each measurement noise variance [33].…”

Section: Posterior Estimation and Covariancementioning

confidence: 99%

Robust sequential adaptive Kalman filter algorithm for ultrashort baseline underwater acoustic positioning

Yang¹,

Zhang²,

Sui³

et al. 2022

Meas. Sci. Technol.

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Affected by dynamic changes in the complex marine environment, ultrashort baseline (USBL) systems may exhibit continuous gross errors in underwater target positioning, resulting in the distortion of the target coordinates. To effectively detect and eliminate continuous gross errors in USBL underwater acoustic positioning, a robust sequential adaptive Kalman filter (RSAKF) algorithm is proposed in this paper. The RSAKF algorithm employs sequential filtering to decompose all measurement updates into multiple submeasurement updates and uses the fading memory weighted average method to estimate the one-step prediction mean square error of the metrics for each submeasurement update. Then, the RSAKF algorithm adopts an adaptive correction method of submeasurement noise variance, which eliminates the influence of continuous gross errors through a more targeted adaptive correction of each submeasurement noise variance. The effectiveness of the algorithm was quantitatively analyzed using a USBL positioning simulation experiment, and the results showed that the continuous gross errors rejection rate of the RSAKF algorithm reached 84.12%. The point error of the RSAKF algorithm is improved by 62.65%, 46.76%, 36.09%, and 26.48% compared with the KF, KF based on Huber (HKF), KF based on IGGIII (IGGKF), and the maximum correntropy Kalman filter (MCKF), respectively. The USBL positioning ROV experiment was conducted in the South China Sea, and the results showed that the RSAKF has the best filtering accuracy. Simulation and actual measurement experiments verified that the RSAKF algorithm can effectively eliminate the influence of continuous gross errors.

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Section: Posterior Estimation and Covariancementioning

confidence: 99%

Robust sequential adaptive Kalman filter algorithm for ultrashort baseline underwater acoustic positioning

Yang¹,

Zhang²,

Sui³

et al. 2022

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…e data is contaminated by the outliers, which is non-Gaussian distribution and heavy-tailed distribution [14]. Two categories of advanced techniques have been developed for treating the observations contaminated by outliers, one is the outlier detection method based on the statistical test and the other is the robust estimation method [15]. Statistical test or model errors, outliers, and biases usually consists of detection, identification, and adaptation (DIA) step which is an important diagnostic tool for data quality control [16].…”

Section: Introductionmentioning

confidence: 99%

A Robust Extended Kalman Filter Applied to Ultrawideband Positioning

Wang

Han

Wang

et al. 2020

Mathematical Problems in Engineering

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Ultrawideband (UWB) is well-suited for indoor positioning due to its high resolution and good penetration through objects. The observation model of UWB positioning is nonlinear. As one of nonlinear filter algorithms, extended Kalman filter (EKF) is widely used to estimate the position. In practical applications, the dynamic estimation is subject to the outliers caused by gross errors. However, the EKF cannot resist the effect of gross errors. The innovation will become abnormally large and the performance and the reliability of the filter algorithm are inevitably influenced. In this study, a robust EKF (REKF) method accompanied by hypothesis test and robust estimation is proposed. To judge the validity of model, the global test based on Mahalanobis distance is implemented to assess whether the test statistical term exceeds the threshold for outlier detection. To reduce and eliminate the effects of the individual outlier, the robust estimation using scheme III of the Institute of Geodesy and Geophysics of China (IGGIII) based on local test of the normalized residual is performed. Meanwhile, three kinds of stochastic models for outliers are expressed by modeling the contaminated distributions. Furthermore, the simulation and measurement experiments are performed to verify the effectiveness and feasibility of the proposed REKF for resisting the outliers. Simulation experiment results are given to demonstrate that the outliers following all the three kinds of contaminated distributions can be detected. The proposed REKF can effectively control the influences of the outliers being treated as systematic errors and large variance random errors. When the outliers come from the thick-tailed distribution, the robust estimation does not play a role, and the REKF are equivalent to the EKF method. The measured experiment results show that the outliers will be generated in the nonline-of-sight environment whose impact is abnormally serious. The robust estimation can provide relatively reliable optimized residuals and control the influences of the outliers caused by gross errors. We can believe that the proposed REKF is effective to resist the effects of outliers and improves the positioning accuracy compared with least-squares (LS) and EKF method. Moreover, the adaptive filter and ranging error model should be considered to compensate the state model errors and ranging systematic errors respectively. Then, the measurement outliers will be detected more correctly, and the robust estimation will be used effectively.

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“…The most popular approaches include the square‐root implementations based on the Cholesky decomposition designed in , the ‘fast triangular' algorithm developed in , the ‘square‐root‐free' implementations in and the most recent development called the sigmaRho filter revealed in . Despite of a great diversity of sequential (scalar measurement case) implementation methods existed for the classical KF, to the best of author's knowledge, there is still no sequential algorithms under the MCC filtering strategy, except some results on the so‐called M‐estimator‐based KF proposed in . This paper is intended to fill in this gap and paves the way for further development of sequential MCC filtering algorithms.…”

Section: Introductionmentioning

confidence: 99%

Sequential Maximum Correntropy Kalman Filtering

Kulikova

2018

Asian Journal of Control

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This paper explores a linear state estimation problem in non-Gaussian setting and suggests a computationally simple estimator based on the maximum correntropy criterion Kalman filter (MCC-KF). The first MCC-KF method was developed in Joseph stabilized form. It requires two n × n and one m × m matrix inversions, where n is a dimension of unknown dynamic state to be estimated, and m is a dimension of available measurement vector. Therefore, the estimator becomes impractical when the system dimensions increase. Our previous work has suggested an improved MCC-KF estimator (IMCC-KF) and its factored-from (square-root) implementations that enhance the MCC-KF estimation quality and numerical robustness against roundoff errors. However, the proposed IMCC-KF and its square-root implementations still require the m × m matrix inversion in each iteration step of the filter. For numerical stability and computational complexity reasons it is preferable to avoid the matrix inversion operation. In this paper, we suggest a new IMCC-KF algorithm that is more accurate and computationally cheaper than the original MCC-KF and previously suggested IMCC-KF. Furthermore, compared with stable square-root algorithms, the new method is also accurate, but less computationally expensive. The results of numerical experiments substantiate the mentioned properties of the new estimator on numerical examples.

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Accuracy improvement by implementing sequential measurement update in robust Kalman filter

Cited by 9 publications

References 24 publications

Robust sequential adaptive Kalman filter algorithm for ultrashort baseline underwater acoustic positioning

Robust sequential adaptive Kalman filter algorithm for ultrashort baseline underwater acoustic positioning

A Robust Extended Kalman Filter Applied to Ultrawideband Positioning

Sequential Maximum Correntropy Kalman Filtering

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