A dynamic outlier point detection method and device

Abstract

The embodiments of the invention provide a dynamic outlier point detection method and device. The method comprises the steps: acquiring data point information, wherein the data point information comprises current-moment first vertical speed of an aircraft and current-moment second vertical speed measured by a radio altimeter mounted on the aircraft; calculating an error value between the first vertical speed and the second vertical speed, and regarding the error value as an error value of data points corresponding to a current moment; calculating a coefficient of variation of the data points according to the error value, and determining that the data points are outlier points if the condition that the obtained coefficient of variation is greater than a threshold value is judged. The device is used for implementing the method. According to the dynamic outlier point detection method and the device, provided by the embodiments of the invention, isolated type outlier points of flight altitude measured data of the aircraft can be detected, and meanwhile, spotted type outlier points of the flight altitude measured data of the aircraft can be detected, so that the accuracy of detection on dynamic outlier points of the flight altitude measured data of the aircraft is improved.

Description

technical field [0001] Embodiments of the present invention relate to the technical field of measurement data detection, and in particular to a dynamic outlier point detection method and device. Background technique [0002] Since small agricultural drones usually operate at ultra-low altitudes, and the flying height is only 3 to 5 meters, the relative height between the body and the ground must be accurately measured during the operation to avoid crashes or collisions with crops due to terrain fluctuations during the operation. collision. Small UAVs usually use radio altimeters, barometric altitude sensors, and GPS satellite navigation receivers to measure and fuse the relative altitude of UAVs. However, when the radio altitude is above the crop canopy, the reflected echoes are easily interfered by the complex shape of the crop canopy, and large outlier errors occur in the collected data, and the radio altimeter not only has isolated outliers, but also has a large number o...

AI Technical Summary

Problems solved by technology

In the actual application process, the relative height of the UAV body is a dynamic state quantity, and each state is a single measurement value, and the statistical characteristics are changing. Therefore, directly using the Wright criterion is not suitable for the determination of dynamic height outliers, especially for The detection and judgment of spotty outliers are even more inappropriate

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