Aiming at the poor location accuracy caused by the harsh and complex underground environment, long strip roadway, limited wireless transmission and sparse anchor nodes, an underground location algorithm based on random forest and compensation for environmental factors was proposed. Firstly, the underground wireless access point (AP) network model and tunnel environment were analyzed, and the fingerprint location algorithm was built. And then the Received Signal Strength (RSS) was analyzed by Kalman Filter algorithm in the offline sampling and real-time positioning stage. Meanwhile, the target speed constraint condition was introduced to reduce the error caused by environmental factors. The experimental results show that the proposed algorithm solves the problem of insufficient location accuracy and large fluctuation affected by environment when the anchor nodes are sparse. At the same time, the average location accuracy reaches three meters, which can satisfy the application of underground rescue, activity track playback, disaster monitoring and positioning. It has high application value in complex underground environment.

针对严峻复杂的地下环境，长条形巷道，有限的无线传输和稀疏的锚节点导致的定位精度差的问题，提出了一种基于随机森林和环境因子补偿的地下定位算法。首先，分析了地下无线接入点的网络模型和隧道环境，并建立了指纹定位算法。然后在离线采样和实时定位阶段，通过卡尔曼滤波算法对接收信号强度（RSS）进行了分析。同时，引入了目标速度约束条件，以减少环境因素引起的误差。实验结果表明，该算法解决了锚节点稀疏时定位精度不高，受环境影响较大的问题。同时，平均定位精度达到三米，可以满足地下救援，活动轨迹回放，灾害监测和定位的应用。在复杂的地下环境中具有较高的应用价值。