近年來無人機技術與應用發展快速，從娛樂、農業、醫療及軍事等，都可以發現其應用。隨著無人機之技術演進，可以利用偵察無人機群進行地面目標物定位。但是由於機翼旋轉和風速等因素，每架無人機可能產生搖擺或偏離其初始預設位置，因而造成定位誤差，因此無人機群成為一動態陣列，與傳統定位研究所使用之靜態陣列大不相同，為了提高定位精確度，如何克服無人機群的幾何擾動問題成為重要的課題。
這項工作提出了一種針對具有大擾動問題的無人機群的改進的校準方法，該方法在定位精度方面明顯優於先前的研究。所提出的方法可以估計無人機群中每個無人機的位置偏差，並利用這些信息通過多信號分類（MUSIC）演算法來改善到達角估計，從而提高地面目標的定位精度。

In recent years, unmanned aerial vehicle(UAV) technology and applications have developed rapidly, and its applications can be found in entertainment, agriculture, medical and military, etc. As the technology of UAVs evolves, it is possible to use a reconnaissance UAV swarm for terrestrial target localization. However, due to factors such as wing rotation and wind speed, each UAV may sway or deviate from its nominal position and results in positioning errors. Therefore, the UAV swarm becomes a positioning problem using a dynamic array that is very different with conventional researches on positioning using a static array. In order to enhance the positioning accuracy, it is important to overcome the disturbance problem of geometric configuration of a UAV swarm.
This work proposes an improved calibration method for a UAV swarm with large disturbance problem, which significantly out performs prior similar researches in terms of the localization accuracy. The proposed method can estimate the position deviation of each UAV in a UAV swarm and use these information to improve the angle of arrival estimation using Multiple Signal Classification (MUSIC) algorithm, and hence improve the localization accuracy of terrestrial targets.

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