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Accuracy improvement by implementing sequential measurement update in robust Kalman filter
Abstract A robust Kalman filter based on Chi square test with sequential measurement update is proposed. This approach can not only handle outliers in part or even individual measurement channel, but can also further improve the accuracy especially when a novel ordering strategy in processing the measurement elements is adopted. The accuracy improvement can be attributed to the higher statistical efficiency, i.e., an increased probability of correctly resisting the outlying measurement elements and retaining the good ones. The accuracy improvement of the proposed method is illustrated by a simulating example.
Accuracy improvement by implementing sequential measurement update in robust Kalman filter
Abstract A robust Kalman filter based on Chi square test with sequential measurement update is proposed. This approach can not only handle outliers in part or even individual measurement channel, but can also further improve the accuracy especially when a novel ordering strategy in processing the measurement elements is adopted. The accuracy improvement can be attributed to the higher statistical efficiency, i.e., an increased probability of correctly resisting the outlying measurement elements and retaining the good ones. The accuracy improvement of the proposed method is illustrated by a simulating example.
Accuracy improvement by implementing sequential measurement update in robust Kalman filter
Chang, Guobin (author) / Wang, Yunjia (author) / Wang, Qianxin (author)
2015
Article (Journal)
English
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