基于航迹方向最大密度估计的红外多目标双站定位

Infrared multi-target dual-station positioning based on maximum density estimation in track direction

为了抑制测量误差对多目标双站定位的影响,本文充分利用目标短时运动航迹点时空分布特点,提出基于航迹最大密度估计的红外运动多目标双站定位方法。首先,基于双站测向射线高程差进行单帧多目标初匹配。然后,基于方向二维直方图,初步估计出目标航迹方向;以此为搜索起点,开展基于Mean Shift的目标航迹方向最大密度估计。最后,基于目标航迹方向进行航迹点真假检验,抑制测量误差对目标定位结果的影响。实验结果表明:本文方法不仅能有效剔除误匹配点,而且能抑制误差偏差点;航迹最大拟合误差小于0.5 m,平均拟合误差小于0.3 m,优于其他算法;对于既有误匹配点又有误差偏差较大定位点的目标,相比于直方图法,本文方法最大拟合误差降低幅度大于50%,平均拟合误差降低27%。本文方法能有效抑制定位误差,在目标三维定位及预测、打靶训练评估等军民用领域具有重要价值。

This study aims to reduce the influence of measurement errors on the positioning of a multi-target in dual-stations. By using the spatio-temporal distribution characteristics of the motion track points of a target over a short time period,an infrared motion multi-target dual-station positioning method is proposed based on the maximum track density estimation. First,single frame multi-target matching is performed based on the elevation difference along direction-finding rays of dual-stations. Then,based on the two-dimensional direction histogram,the target track direction is preliminarily estimated,following which the maximum density of the target track direction is determined based on the mean shift. Finally,the authenticity of the track point is validated based on the target track direction to reduce the influence of measurement errors on the target positioning result. The experimental results reveal that the proposed method effectively eliminates the mismatch point and reduces the error deviation point. The maximum fit error of the track is less than 0. 5 m,and the average fit error is less than 0. 3 m,which represent improvements on existing algorithms. For targets that exhibit both mismatched points and larger error deviations compared with those of the histogram method,the maximum fitting error of the proposed method is reduced by more than 50%,and the average fitting error is reduced by 27%. Thus,the proposed method can effectively reduce the positioning error,which has important applications in military and civilian fields,such as three-dimensional positioning,target prediction,and hooting training evaluation.