Indoor fingerprint localization method based on FDE-IRF
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摘要: 针对传统指纹库存在建立工作量大以及随机森林匹配误差大等问题，提出了一种基于指纹库自动扩充的改进随机森林指纹定位方法(FDE-IRF)以提升指纹库构建的效率和指纹匹配的精度. 该方法对传统全采样构建指纹库方法和随机森林回归定位方法进行改进，稀疏采样多时间段的指纹数据和Kriging插值方法组合补全未采样指纹点，提升建库效率，得到强代表性的指纹库. 同时，利用决策树加权策略改进传统随机森林平均投票的方式，根据袋外数据评估决策树的预测误差，分配相应的权重，提高该算法的回归准确率. 实验结果表明：该方法的平均定位误差为1.26 m，其误差值比同类方法至少降低14.3%，验证了算法的准确性和有效性.Abstract: Aiming at the problems of heary work during establishment of traditional fingerprint database and the large matching error of the traditional random forest, an improved random forest localization method was proposed based on automatic fingerprint database expansion (FDE-IRF) to enhance the efficiency of fingerprint database construction and the accuracy of fingerprint matching. This method improved the traditional all sampling method to construct fingerprint database and the traditional random forest regression positioning method. The combination of sparse sampling fingerprint data of multiple time periods and Kriging interpolation method to complete unsampled fingerprint points improves the efficiency of database construction and gets a strong representative fingerprint database. At the same time, the decision tree weighting strategy is used to improve the average voting method in the traditional random forest, and the data out of bag is used to evaluate the prediction error of the decision tree and assign the corresponding weight, which improves the regression accuracy of the algorithm. The experimental results shows that the average positioning error of the proposed method is 1.26 m, which is at least 14.3% lower than that of similar methods, and verifies the accuracy and effectiveness of the proposed method.
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表 1 指纹库数据
位置坐标 指纹数据 $ ({x}_{1},{y}_{1})$ $ ({\rm{RSS}}_{1,1}^{1},{\rm{RSS}}_{1,2}^{1},\cdots ,{\rm{RSS}}_{1,n}^{1})$ $\vdots $ $ ({\rm{RSS}}_{m,1}^{1},{\rm{RSS}}_{m,2}^{1},\cdots ,{\rm{RSS}}_{m,n}^{1})$ $ ({x}_{2},{y}_{2})$ $ ({\rm{RSS}}_{1,1}^{2},{\rm{RSS}}_{1,2}^{2},\cdots ,{\rm{RSS}}_{1,n}^{2})$ $\vdots $ $ ({\rm{RSS}}_{m,1}^{2},{\rm{RSS}}_{m,2}^{2},\cdots ,{\rm{RSS}}_{m,n}^{2})$ $\vdots $ $\vdots $ $ ({x}_{s},{y}_{s})$ $ ({\rm{RSS}}_{1,1}^{s},{\rm{RSS}}_{1,2}^{s},\cdots ,{\rm{RSS}}_{1,n}^{s})$ $\vdots $ $ ({\rm{RSS}}_{m,1}^{s},{\rm{RSS}}_{m,2}^{s},\cdots ,{\rm{RSS}}_{m,n}^{s})$ 注：$m$表示第$m$条指纹；$ ({x}_{s},{y}_{s})$表示第$s$个位置处的坐标； ${\rm{RSS}}_{m,n}^s$表示第$s$个位置处第$m$条指纹的第$n$个AP的RSS值. 
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