The traditional grey wolf (GW) positioning algorithm enjoys a good positioning effect, but its improvement of localization accuracy is limited due to the lack of ranging information between the labels. This paper presents a novel GW cooperative positioning algorithm that can improve the localization accuracy. Firstly, a new notion of fitness is established which takes the ranging information between the labels into account. Then the ranging information from the label to the base station is used to acquire a good initial location of the label, based on the traditional GW positioning algorithm. Finally, the entire ranging information and the acquired initial location are employed to obtain a precise positioning. Simulation results demonstrate that when compared to the traditional GW positioning algorithm, the positioning accuracy of the proposed algorithm shows different improvement effects under different conditions, but the improvement degree is more than 34%.

传统的灰狼（GW）定位算法具有良好的定位效果，但由于缺乏标签之间的测距信息，其定位精度的提高有限。本文提出了一种新的GW协同定位算法，可以提高定位精度。首先，建立了一个新的适应度概念，它考虑了标签之间的测距信息。然后基于传统的GW定位算法，利用标签到基站的测距信息来获取标签的良好初始位置。最后，利用整个测距信息和获取的初始位置来获得精确定位。仿真结果表明，与传统的GW定位算法相比，