基于运动模型的低空非合作无人机目标识别

为保障低空安全，在利用雷达数据探测无人机目标的同时剔除飞鸟等虚警信息，提出了一种基于运动模型的低空非合作无人机目标识别方法，作为已有目标跟踪方法应用的拓展。首先，建立多种运动模型模拟无人机和飞鸟目标运动；然后，基于多种运动模型进行目标跟踪，并估计各种运动模型的出现概率；最后，以各种运动模型在连续时间内出现概率的方差均值来度量目标运动模型的转换频率。通过对仿真数据和机场低空监视雷达实测数据的处理，所提方法能够在杂波环境中跟踪无人机目标并剔除飞鸟目标，进一步验证了其有效性和实用性。 

Non-cooperative UAV target recognition in low-altitude airspace based on motion model

To guarantee the safety of low-altitude airspace, a target recognition method based on motion model was proposed for the non-cooperative UAV target in low-altitude airspace, as an extension of the application of the existing target tracking algorithm, which could detect the UAV and reject the false alarms such as flying birds with radar data. Firstly, multiple motion models were established to simulate the movement of UAV and flying bird targets. Secondly, the targets were tracked with multiple motion models and the appearance probabilities of these models were estimated. Thirdly, the transformation frequency between target motion models was measured by the mean variance of the appearance probabilities of multiple models in continuous time domain. By processing the simulation data and the measured data of the airport low-altitude surveillance radar, the method can track the UAV target in cluttered environment and eliminate the flying bird target, further verifying its effectiveness and practicability.