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首页> 美国卫生研究院文献>Sensors (Basel Switzerland) >Real-Time Multi-Target Localization from Unmanned Aerial Vehicles
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Real-Time Multi-Target Localization from Unmanned Aerial Vehicles

机译：无人机实时多目标定位

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摘要

In order to improve the reconnaissance efficiency of unmanned aerial vehicle (UAV) electro-optical stabilized imaging systems, a real-time multi-target localization scheme based on an UAV electro-optical stabilized imaging system is proposed. First, a target location model is studied. Then, the geodetic coordinates of multi-targets are calculated using the homogeneous coordinate transformation. On the basis of this, two methods which can improve the accuracy of the multi-target localization are proposed: (1) the real-time zoom lens distortion correction method; (2) a recursive least squares (RLS) filtering method based on UAV dead reckoning. The multi-target localization error model is established using Monte Carlo theory. In an actual flight, the UAV flight altitude is 1140 m. The multi-target localization results are within the range of allowable error. After we use a lens distortion correction method in a single image, the circular error probability (CEP) of the multi-target localization is reduced by 7%, and 50 targets can be located at the same time. The RLS algorithm can adaptively estimate the location data based on multiple images. Compared with multi-target localization based on a single image, CEP of the multi-target localization using RLS is reduced by 25%. The proposed method can be implemented on a small circuit board to operate in real time. This research is expected to significantly benefit small UAVs which need multi-target geo-location functions.
机译：为了提高无人机电光稳定成像系统的侦察效率，提出了一种基于无人机电光稳定成像系统的实时多目标定位方案。首先，研究目标位置模型。然后，使用齐次坐标变换来计算多目标的大地坐标。在此基础上，提出了两种可以提高多目标定位精度的方法：（1）实时变焦镜头畸变校正方法； （2）一种基于无人机航位推算的递推最小二乘（RLS）滤波方法。利用蒙特卡洛理论建立了多目标定位误差模型。在实际飞行中，无人机飞行高度为1140 m。多目标定位结果在允许误差范围内。在单幅图像中使用镜头畸变校正方法后，多目标定位的圆误差概率（CEP）降低了7％，并且可以同时定位50个目标。 RLS算法可以基于多个图像自适应地估计位置数据。与基于单个图像的多目标定位相比，使用RLS进行的多目标定位的CEP降低了25％。所提出的方法可以在小型电路板上实现以实时运行。这项研究有望使需要多目标地理位置功能的小型无人机显着受益。

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