Real-time and accurate location detection is a key link to ensure the safety of operating machines and workers in production and life. Compared with traditional static multi-anchor nodes, mobile anchor node assisted localization is greener and more energy-saving. In this paper, we first propose a static trajectory based on a light reflection model. Compared with other static models, this model has fewer times in the field, overcomes the collinearity problem and uniform beacon distribution, and ensures that all sensor nodes can receive good enough beacon quality for localization. Secondly, an RSSI-based improved weighted centroid localization algorithm and an RSSI-based improved weighted centroid collaborative localization algorithm are proposed. The two-strategy optimal location beacon set screening method is used to reduce location misjudgment. In order to improve the accuracy of centroid localization, a weighted centroid localization algorithm based on distance and hop number is designed. Moreover, a collaborative localization strategy is aiming at improving beacon density. Experimental results show that both the algorithm and static trajectory can guarantee better beacon coverage rate and localization success rate under different experimental conditions, and at the same time have higher accuracy.

实时准确的位置检测是保障作业机器和工人生产生活安全的关键环节。与传统静态多锚节点相比，移动锚节点辅助定位更加绿色、节能。在本文中，我们首先提出了基于光反射模型的静态轨迹。与其他静态模型相比，该模型在场次数较少，克服了共线性问题和均匀的信标分布，并保证所有传感器节点都能收到足够好的信标质量进行定位。其次，提出了基于RSSI的改进加权质心定位算法和基于RSSI的改进加权质心协同定位算法。采用双策略最优位置信标组筛选方法，减少位置误判。为了提高质心定位的精度，设计了一种基于距离和跳数的加权质心定位算法。此外，协作本地化策略旨在提高信标密度。实验结果表明，该算法和静态轨迹在不同实验条件下都能保证较好的信标覆盖率和定位成功率，同时具有较高的精度。