基于改进LightGBM的室内指纹定位算法

针对室内定位算法在定位时所用时间较长和定位精度较低的问题,提出了一种基于改进LightGBM算法的室内定位算法。该算法首先针对指纹库中的数据进行预处理,通过KNN算法去除异常点和离群点,降低环境噪声干扰,提高数据可靠性。接下来,将样本集划分为训练集和测试集,使用LightGBM算法对进行建模。同时,使用遗传算法调整LightGBM算法中的参数,并根据适应度函数寻找最优参数,得到LightGBM+GA坐标预测模型。最后,根据优化后的参数建立预测模型实现坐标预测。实验结果表明,该算法在WiFi定位的精度上较与XGBoost算法提高0.1m,相较于GBDT算法提高0.19m,在定位时间上,LightGBM+GA算法比GBDT算法快5.10s,比XGBoost算法快5.97s,具有较好的实用性。

Indoor fingerprint location algorithm based on improved LightGBM

Aiming at the problems of long time and low positioning accuracy of indoor positioning algorithm, an indoor positioning algorithm based on improved LightGBM algorithm is proposed. The algorithm first preprocesses the data in the fingerprint database, and removes outliers and outliers through KNN algorithm to reduce environmental noise interference and improve data reliability. Next, divide the sample set into training set and test set, and use LightGBM algorithm to model. At the same time, genetic algorithm is used to adjust the parameters in LightGBM algorithm, and the optimal parameters are found according to the fitness function to obtain the LightGBM+GA coordinate prediction model. Finally, a prediction model is established according to the optimized parameters to realize coordinate prediction. The experimental results show that the algorithm improves the accuracy of WiFi positioning by 0.1m compared with XGBoost algorithm and 0.19m compared with GBDT algorithm. In terms of positioning time, LightGBM+GA algorithm is 5.10s faster than GBDT algorithm and 5.97s faster than XGBoost algorithm, which has good practicability. ]