基于改进KNN-RF的信息补全算法

Information completion algorithm based on improved KNN-RF

针对室内指纹定位指纹库数据在实际环境中存在数据缺失导致定位误差大的问题,本文提出了一种改进距离公式的K近邻-随机森林的信息补全算法。首先,采用高斯滤波对收集的指纹数据进行预处理,去除干扰数据项,提高数据可靠性。其次,在将指纹数据划分为训练集和测试集的基础上,采用结合欧氏距离和曼哈顿距离的KNN算法获得近邻集合样本,随后用RF算法对近邻集合训练进行优化,再把各个决策树的预测结果取平均值,得到缺失数据的预测值。最后,将改进的补全算法与KNN、改进的KNN、RF和KNN-RF补全算法进行对比。实验结果表明,本文的改进补全算法的预测准确率和精度均优于其他算法,预测的准确率达91.3%。同时本文补全算法的指纹库平均定位误差为1.82 m,相较于其他补全算法的指纹库定位误差降低了1.6%~7.2%,定位性能更好。

This paper proposes an information complementation algorithm of K nearest neighbor-random forest with an improved distance formula,aiming at the problem of indoor fingerprint localization fingerprint database data in the real environment with missing data leading to large positioning errors.First,the gathered fingerprint data is preprocessed using Gaussian filtering to eliminate interfering data points and enhance data dependability.Second,the nearest-neighbor set is sampled using the KNN algorithm,which combines Manhattan distance and Euclidean distance.The RF algorithm is then used to optimize the training of the nearest-neighbor set,and the prediction results of each individual decision tree are averaged to determine the predicted values of the missing data.This process is based on the division of the fingerprint data into training and testing sets.Finally,the improved complementary algorithm is compared with KNN,improved KNN,RF and KNN-RF complementary algorithms.The experimental results demonstrate that the modified complementary method in this study has superior prediction accuracy and precision than other algorithms,with a prediction accuracy of 91.3%.In the meantime,the fingerprint library of this paper′s complimentary algorithm has an average positioning error of 1.82 m,which is 1.6%~7.2%less than that of other complementary algorithms,and the positioning performance is improved.