Indoor Localization Method Based on Subspace Segmentation and SSA-XGBoost
When using location fingerprint for real-time indoor positioning,redundant AP will occur due to multipath effect,signal bloc-king or unstable wireless AP itself,which will affect the final positioning effect. For this reason,an indoor location method is presented, which is based on subarea segmentation combined with sparrow search algorithm( SSA) optimization. In the offline training phase,the ar-ea to be located is divided into several subdomains by using the improved FCM( Fuzzy C-means) algorithm and the regional correlation coefficient index,and the optimal AP set is selected for each subdomain by AP optimization. Because the performance of the algorithm is susceptible to initial parameter problems,the sparrow search algorithm is used to optimize the initial parameters to obtain relatively opti-mal parameters,and the positioning model is built for each subarea. In the online positioning stage. the subareas of the target points are acquired by matching the cluster centers of the subareas,and finally the positioning model of the subareas is used to predict the location of the target points. Comparing with other locating algorithms,the average error of the proposed algorithm is reduced by 14.7％,22.4％, 37.1％,respectively,proving that the proposed method has better locating effect in the actual environment than other algorithms.
万方数据
维普
