基于整形多卡尔曼滤波模型的GPS实时变形分析(英文)

提出的整形多卡尔曼滤波模型可进行形变的实时检测并提高其可靠性。多卡尔曼滤波模型的扩展主要针对GNSS时间形变序列含有有色噪声的情况,而多卡尔曼滤波模型只能应用于白噪声的情况下,故采用整形滤波扩展多卡尔曼滤波模型去除有色噪声的影响。提出的模型可以提高结果的精度且实时进行变形监测,可用于灾害预警系统。通过对比该模型与其他模型(卡尔曼滤波模型,整形卡尔曼滤波模型,多卡尔曼滤波模型)发现,所改进的模型在检测形变的实时性和可靠性方面优于其他模型。

Multiple Kalman filters model with shaping filter GPS real-time deformation analysis

In order to detect the deformation in real-time of the GPS time series and improve its reliability, the multiple Kalman filters model with shaping filter was proposed. Two problems were solved: firstly, because the GPS real-time deformation series with a high sampling rate contain coloured noise, the multiple Kalman filter model requires the white noise, and the multiple Kalman filters model is augmented by a shaping filter in order to reduce the colored noise; secondly, the multiple Kalman filters model with shaping filter can detect the deformation epoch in real-time and improve the quality of GPS measurements for the real-time deformation applications. Based on the comparisons of the applications in different GPS time series with different models, the advantages of the proposed model were illustrated. The proposed model can reduce the colored noise, detect the smaller changes, and improve the precision of the detected deformation epoch.