本文提出并验证了一种基于光源-探测器阵列码和人工神经网络的空间定位方法.在探测区域的上方放置若干发光二极管，并在下方随机排列了多个光电探测单元.物体遮光形成的光强分布可以视为表征空间位置的特征.将光强分布数据利用预先搭建的神经网络处理和学习，实现从光强分布到物体空间坐标的映射关系，以实现物体的高精度、快响应空间定位. 实验利用了 6 个发光二极管和 8组光电池搭建了演示系统，通过实际测量进行了空间定位.

In this paper，a spatial positioning method based on light source-detector array

code and artificial neural network is proposed and verified. A number of LEDs are placed above

the detection area，and a number of photoelectric detection units are randomly arranged below.

The distribution of light intensity caused by blocking light can be regarded as the characteristic

of spatial position. The light intensity distribution data is processed and learned

by using the pre-built neural network to realize the mapping relationship between the light

intensity distribution and the spatial coordinates of the object，so as to realize the spatial

positioning of the object with high precision and fast response. In the experiment，6 LEDs and 8

photovoltaic cells are used to build a demonstration system，and spatial positioning is carried out

through actual measurement.