In the process of social progress and development, the wide application of various technologies has provided convenience for the development of various fields, and the development of motion capture technology has provided reliable technical support for the development of basketball. In the development process of indoor positioning technology, although many advanced technologies can help people obtain some key data, there are still many shortcomings. This article analyzes the shortcomings of various technologies in the process of positioning, and proposes an indoor positioning method of WKNN based on the spatial characteristics of the object for the problems of large positioning results and low accuracy. This is to a certain extent Solved many problems. In practice, the Wi-Fi fingerprint database used by the WKNN algorithm proposed for spatial features is consistent with the traditional WKNN algorithm, but the results calculated by the traditional WKNN algorithm may have large errors and people cannot judge the calculation. The newly proposed algorithm can solve the problem of large calculation result span and inaccurate positioning caused by the selection of multiple target points in the same area for analysis by traditional algorithms. In order to reduce the complexity of subsequent research work, this article also focuses on the space the characteristics of the object are analyzed, and the constraint points are specified during the calculation process, so that the result distribution of the traditional WKNN algorithm can be restricted to prevent large interval jumps. Specific experimental analysis can prove that the method proposed in this article can solve the problem of low positioning accuracy to a certain extent.

在社会进步发展过程中，各种技术的广泛应用为各个领域的发展提供了便利，动作捕捉技术的发展为篮球运动的发展提供了可靠的技术支撑。在室内定位技术的发展过程中，虽然有很多先进的技术可以帮助人们获取一些关键数据，但仍然存在很多不足。本文分析了各种技术在定位过程中的不足，针对定位结果大、精度低的问题，提出了一种基于物体空间特征的WKNN室内定位方法。这在一定程度上解决了很多问题。在实践中，提出的WKNN算法用于空间特征的Wi-Fi指纹数据库与传统WKNN算法一致，但传统WKNN算法计算的结果可能存在较大误差，人们无法判断计算结果。新提出的算法可以解决传统算法在同一区域选择多个目标点进行分析而导致的计算结果跨度大和定位不准确的问题。为了降低后续研究工作的复杂度，本文还重点分析了空间对象的特征，并在计算过程中指定了约束点，从而限制了传统WKNN算法的结果分布防止大的间隔跳跃。