Extracting Time-Varying Mean Component of Non-Stationary Winds Utilizing Vondrak Filter and Genetic Algorithm: A Wind Engineering Perspective

The time-varying mean (TVM) component plays a vital role in the characterization of non-stationary winds, whereas it is difficult to extract the TVM accurately or to validate it quantitively. To deal with this problem, this paper first develops two additional conditions for the TVM extraction from the perspective of structural wind-induced vibration response, then presents an approach, based on the combination of Vondrak filter and genetic algorithm (Vondrak-G), to derive the optimal TVM from non-stationary wind speed records as well as its turbulence characteristics (i.e. gust factor, turbulence intensity, and turbulence integral length scale). Furthermore, the wind characteristics obtained by the Vondrak-G approach are compared with those by a conventional approach derived for stationary winds, demonstrating that the results by the Vondrak-G approach are evidently more accurate. This paper aims to provide an effective method for accurately extracting the TVM and then evaluating wind characteristics of the non-stationary wind.

APPLICATION OF MULTI-FACETED FUNCTION MODEL BASED ON VONDRAK FILTER OPTIMIZATION IN UAV AERIAL IMAGE HEIGHT CORRECTION

With the rapid development of drone technology and digital camera technology, the method of obtaining high-precision coordinates based on UAV aerial photogrammetry technology is popular. The plane coordinate accuracy of the aerial image of the drone has been able to meet the needs of practical applications, but the elevation accuracy is generally low. Aiming at the low elevation accuracy of UAV aerial photogrammetry, a multi-face function fitting method based on Vondrak filter optimization was proposed. The improved fitting model was used to obtain the elevation correction value of the aerial image, thereby obtaining high-precision image elevation data. In this paper, based on the traditional multi-face function fitting method, some known points were used to model and find the difference between the measured elevation value and the measured elevation. The Vondrak filter was used to smooth the fitting result. Finally, a small number of known elevation points were used for checking, so that the obtained elevation was compared with the actual elevation. The experimental comparison showed that the improved multi-face function fitting method used Vondrak filter was improved by 34.76% compared with the quadric surface fitting, and improved by 14.48% compared with the optimized cubic surface fitting method. Research shows that the multi-faceted function method based on Vondrak filtering is superior to the traditional elevation correction method. The experiment verifies the effectiveness and feasibility of the improved method, and provides some reference value for the research of aerial image elevation correction model.

Data Preprocessing of Magnetic Suspension Gyro-Total-Station by Vondrak Filter

For Magnetic Suspension Gyro-total-station has vulnerable to outside interference factors, there are some random drifting containing in measurements which are unable to establish its mathematical model. Vondrak filter which does not require the model is used to pre-process measurements of Magnetic Suspension Gyro-total-station. In this paper a high-precision astronomical baseline is established in Xi'an, and the gyro azimuth is tested eight times in baseline. 40,000 north-seeking torque of the first and second place is filtered by the Vondrak filter for each test. The results show that the burr of data is reduced after filtered, and the filtered values reflect the trends of gyro north-seeking. Compared with the root mean square (RMS) of the measurements, RMS of Vondrak filter is decreased, the data is denser. Vondrak filter can effectively eliminate the random drifting containing in measurements, retain useful information in the maximum extent, and improve the accuracy of true north azimuth.