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A New Adaptive Robust Unscented Kalman Filter for Improving the Accuracy of Target Tracking | IEEE Journals & Magazine | IEEE Xplore

A New Adaptive Robust Unscented Kalman Filter for Improving the Accuracy of Target Tracking

Open Access

MSEs of different estimations: the proposed QS-ARUKF can achieve a substantial performance improvement over the conventional filter.

Abstract:

In target tracking, the tracking process needs to constantly update the data information. However, during data acquisition and transmission of sensors, outliers may occur...Show More

Abstract:

In target tracking, the tracking process needs to constantly update the data information. However, during data acquisition and transmission of sensors, outliers may occur frequently, and the model is disturbed by non-Gaussian noise, that affects the performance of system state estimation. In this paper, a new filtering algorithm is proposed based on QR decomposition and singular value decomposition (SVD) method, namely adaptive robust unscented Kalman filter (QS-ARUKF) to suppress the interference of outliers, nonGaussian noise as well as a model error to achieve high accuracy state estimation. An adaptive filtering algorithm based on strong tracking idea is used in modifying the state equation of unscented Kalman filter (UKF), so that the algorithm can effectively improve the tracking ability of the state model. By using the robust filtering method to construct a new cost function used to modify the measurement covariance formula of the Kalman filter, the error of measurement model can be effectively suppressed. The QR decomposition is introduced to the time update and measurement update to avoid the covariance non-positive definite. We propose the SVD method to address the problem of numerical sensitivity in the filtering process. The purpose of this method is to replace the calculation of the inverse of the filter gain matrix and further improve the robustness of the algorithm. The simulation results showed that the proposed algorithm has higher accuracy and better robustness than the traditional filtering method.
MSEs of different estimations: the proposed QS-ARUKF can achieve a substantial performance improvement over the conventional filter.
Published in: IEEE Access ( Volume: 7)
Page(s): 77476 - 77489
Date of Publication: 10 June 2019
Electronic ISSN: 2169-3536

Funding Agency:


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