基于自适应坏点剔除的多点定位技术

Multi-point Positioning Based on Adaptive Bad Pixel Rejection

对机场入侵无人机进行监视优化定位,可降低机场运行安全风险。由于无人机飞行具有低、慢、小等特征,机场场面监视信号容易被地面障碍物遮挡。传统定位方法主要采用参考特征点定位,对于参考特征不同的非移动目标,在迭代过程中会产生大量定位误差。根据入侵无人机特征,提出一种基于自适应坏点剔除的泰勒级数展开到达时间差(time difference of arrival,TDOA)定位算法。采用TDOA测量值进行多点定位,构建自适应滤波器对泰勒级数展开初始参考点进行剔除坏点处理,有效地改善了算法收敛性和求解效率。实验结果表明,自适应坏点剔除算法的定位精度接近基于实际位置的泰勒级数展开算法,收敛速度更快,在地面站相对无人机位置不佳时,新算法的稳定性和定位精度更优,满足机场对入侵无人机监视定位的精度要求。

Monitoring and optimizing the positioning of the UAV in airport can reduce the safety risk of airport operation. Due to the low height, slow speed, and small size features of UAV flight, the surveillance signals of airport scene could be easily obscured by ground obstructions. Traditional tracking method uses mainly the reference feature point location, and for the non-moving object with different reference features, in which a large number of positioning errors would be generated during the iteration process. According to the features of intruding UAVs, a TDOA(time difference of arrival) positioning algorithm based on Taylor series for adaptive bad pixels removal was proposed. TDOA measurements were used to locate multiple points;and an adaptive filter was constructed to remove bad pixels from the initial reference points of the Taylor series expansion, which effectively improved the convergence and efficiency of the algorithm. The simulation results show that the accuracy of adaptive bad pixel reject algorithm was close to the Taylor series expansion algorithm based on the actual position, and the convergence speed was faster. When the position of the ground station relative to the UAV was poor, the stability and positioning accuracy of the new algorithm were more excellent. The new method has been proved practical to meet the positioning accuracy requirements in airport surveillance in case of UAV invasion.