A correlational inference-based unscented total Kalman filter for integrated navigation

Yu, Hongkai; Jian, Wang; Wang, Bin

doi:10.1080/00396265.2020.1739409

Cited by 2 publications

(1 citation statement)

References 22 publications

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“…These factors can also be used in predicting the mining progress. Moreover, there are other Kalman filter methods, such as extended Kalman filter [32,33], unscented Kalman filter [34][35][36], and total Kalman filter [37,38]. These methods can be widely used in prediction models.…”

Section: Discussionmentioning

confidence: 99%

An Improved Knothe Time Function Model in the Prediction of Ground Mining Subsidence by Using the Kalman Filter Method

Zhang

2022

Geofluids

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The Knothe time function is a classical method in predicting the ground mining subsidence. Nevertheless, it does not take the observation data into account in the prediction process. The Kalman filter method can solve this issue at large extent. Taking a coal mining work face of Xishan Coalfield as an example, this research compares the performance of the traditional Knothe time function and that of the improved Knothe time function by using the Kalman filter method. The comparison results show that through an improvement by using the Kalman filter method, the RMSE is improved from 133.4 mm to 78.3 mm; ME, from 91.9 mm to 3.1 mm; and the relative error, from 8.1% to 5.7%. Meanwhile, the improved model has good astringency. These verify that the improved model has higher accuracy and reliability. Hence, this research presents an effective method in predicting ground subsidence of mining area by improving the Knothe time function using the Kalman filter method.

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Section: Discussionmentioning

confidence: 99%

An Improved Knothe Time Function Model in the Prediction of Ground Mining Subsidence by Using the Kalman Filter Method

Zhang

2022

Geofluids

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Ground Subsidence Monitoring in a Mining Area Based on Mountainous Time Function and EnKF Methods Using GPS Data

Zhang

2022

Remote Sensing

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Ground subsidence is an important geomorphological phenomenon in mining areas. It is difficult to monitor and predict ground subsidence with high precision, especially in mountainous mining areas. Taking the mining workface of a mountainous coalfield in Taiyuan City, in the Shanxi Province of China as an example, this research selects five typical points from GPS observation data along the strike section. Based on the materials, the ground subsidence processes at these typical points are monitored and predicted using the mountainous time function method. Acquired from the mountains time function is a recurrence equation, which is regarded as the state equation, and the Ensemble Kalman (EnKF) method is conducted accordingly. Finally, the performance of the two methods is evaluated and compared using error curves and indexes. This research presents a recurrence equation based on the mountainous time function method and establishes the EnKF method for ground subsidence monitoring and prediction. Meanwhile, compared to the mountainous time function method, the values of the ME, MAE, RMSE and MAPE indexes are largely improved for the EnKF method. Hence, this research not only presents an effective method for ground subsidence monitoring in mountainous mining areas, but also provides theoretical support for safe coal mining and environmental protection.

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A correlational inference-based unscented total Kalman filter for integrated navigation

Cited by 2 publications

References 22 publications

An Improved Knothe Time Function Model in the Prediction of Ground Mining Subsidence by Using the Kalman Filter Method

An Improved Knothe Time Function Model in the Prediction of Ground Mining Subsidence by Using the Kalman Filter Method

Ground Subsidence Monitoring in a Mining Area Based on Mountainous Time Function and EnKF Methods Using GPS Data

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