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针对非协作式无人机检测与避障系统,采用多传感器进行信息融合的方式进行检测与跟踪,提出了采用正交积分点卡尔曼滤波(QKF)实时跟踪运动目标以提高检测精度和增强有效性。首先,对设计的检测与避障系统进行了简述,由两个子系统构成:由捷联惯性导航系统(SINS)与GPS组成的导航单元及由雷达和光电传感器组成的检测单元。其次,以拐弯模型与Singer模型两个机动运动模型为例测试了QKF算法跟踪检测障碍物的性能,并与无迹卡尔曼滤波(UKF)进行比较。仿真结果表明,相比于UKF算法,QKF算法可以更快速、更准确的检测与跟踪目标。
Aiming at non-cooperative UAV detection and obstacle avoidance system, multi-sensor information fusion is used to detect and track the moving target. The quadrature integral Kalman filter (QKF) is proposed to track the moving target in real time to improve the detection accuracy and enhance effectiveness Sex. First of all, the design of detection and obstacle avoidance system is briefly described. It consists of two subsystems: a navigation unit consisting of SINS and GPS, and a detection unit consisting of radar and photoelectric sensor. Secondly, the performance of the QKF algorithm for tracking obstacle detection is tested by using two mobility models of turning model and Singer model, and compared with unscented Kalman filter (UKF). Simulation results show that compared with the UKF algorithm, QKF algorithm can detect and track the target more quickly and accurately.