针对WiFi室内指纹定位中指纹库易受环境变化影响、定位精度低等问题，提出了一种LightGBM + CatBoost +XGBoost集成学习加权融合的室内指纹定位算法。在离线阶段，采用基于中位数绝对偏差的Boxplot滤波，有效过滤了指纹库中的异常值。然后对过滤后指纹库的缺失值，采用了K近邻（K-nearest neighbors, KNN）填补指纹库中缺失值，确保指纹库的稳定性。在在线阶段，结合LightGBM（light gradient boosting machine）、CatBoost（categorical boosting）和XGBoost（extreme gradient boosting）3种集成学习模型，通过混沌自适应JAYA算法动态调整模型权重，构建加权融合的坐标预测模型。实验结果表明，提出算法的平均定位误差为1.38 m，相较于粒子群优化（particle swarm optimization, PSO）-极限学习机（extreme learning machine, ELM）、KNN、LightGBM、CatBoost、XGBoost和KNN + XGBoost算法降低了6.52% ~ 37.7%，为室内定位提供了一种精确且鲁棒的解决方案。

To address the issues of fingerprint database vulnerability to environmental changes and low positioning accuracy in WiFi indoor fingerprinting localization, an indoor fingerprinting localization algorithm based on the weighted fusion of LightGBM, CatBoost, and XGBoost ensemble learning was proposed. In the offline phase, a Boxplot filter based on median absolute deviation was used to effectively filter out outliers from the fingerprint database. The missing values in the filtered fingerprint database were then imputed using KNN (K-nearest neighbors), ensuring the stability of the fingerprint database. In the online phase, three ensemble learning models: LightGBM (light gradient boosting machine), CatBoost (categorical boosting), and XGBoost (extreme gradient boosting): were combined. The chaotic adaptive JAYA algorithm was used to dynamically adjust model weights, constructing a weighted fusion coordinate prediction model. Experimental results show that the proposed algorithm achieves an average positioning error of 1.38 m, reducing the error by 6.52% to 37.7% compared to PSO (particle swarm optimization)-ELM (extreme learning machine), KNN, LightGBM, CatBoost, XGBoost, and KNN + XGBoost algorithms, providing a robust and accurate solution for indoor positioning.