针对大尺寸、长距离、非接触条件下的目标定位技术需求，提出了一种基于视觉测量的目标定位技术。许多视觉测量方法寻求在PnP算法改进上得到更高精度，而没有考虑到特征点数目增加对测量精度造成的影响，基于此，通过引入测量基线的概念设计出一种精度可定量提高的合作式靶标。首先通过测量模型的建立和精度分析，利用误差传播理论得出了n点式靶标测量精度的函数解析式，进一步详细分析与验证了各影响项所带来的影响，并讨论了合作靶标特征点排布形式对精度的影响情况，在此基础上进行了靶标布局以及结构参数的优化以进一步提高测量精度。实验结果表明，以所设计的16点环阵式靶标为例，其重投影误差较同尺寸P4P靶标减少了约50.3%，立体靶标则进一步将重投影误差降低了39.2%。该技术不仅可用于特殊环境下的目标定位识别，更适用于复杂系统中的单元状态监测。

A scalable visual measurement was presented to meet the technical requirements of target location under large size and long distance non-contact conditions. Many visual measurement methods seek to obtain higher accuracy on the improvement of PnP algorithm, but do not consider the impact of increasing the number of feature points. Thus, a cooperative target design by quantitatively expanding the number of feature points to achieve higher positioning accuracy was proposed. Firstly, through the establishment of measurement model and accuracy analysis, the function analysis formula of measurement accuracy of n-point target was obtained by using error propagation theory. Then the accuracy of the model was verified through simulation experiments, and the effect of the arrangement of the feature points of the cooperative target on accuracy was further discussed. Subsequently, the target layout and structural parameters were optimized. The experimental results show that the reprojection error of the designed 16-point ring array target is reduced by about 50.3% compared with the same size P4P target, and the stereo target further reduces the reprojection error by 39.2%. This technology can be used not only for target positioning in special environments, but also for unit state monitoring in complex systems.