We propose an approach for fingerprinting-based positioning which reduces the data requirements and computational complexity of the online positioning stage. It is based on a segmentation of the entire region of interest into subregions, identification of candidate subregions during the online-stage, and position estimation using a preselected subset of relevant features. The subregion selection uses a modified Jaccard index which quantifies the similarity between the features observed by the user and those available within the reference fingerprint map. The adaptive feature selection is achieved using an adaptive forward-backward greedy search which determines a subset of features for each subregion, relevant with respect to a given fingerprinting-based positioning method. In an empirical study using signals of opportunity for fingerprinting the proposed subregion and feature selection reduce the processing time during the online-stage by a factor of about 10 while the positioning accuracy does not deteriorate significantly. In fact, in one of the two study cases the 90th percentile of the circular error increased by 7.5% while in the other study case we even found a reduction of the corresponding circular error by 30%.
翻译:我们建议采用基于指纹的定位方法,以减少数据要求和在线定位阶段的计算复杂性,其基础是将整个感兴趣的区域分割成次区域,在在线阶段确定候选次区域,并利用事先选定的相关特征子集进行职位估计;次区域选择使用经修改的“记分卡”指数,该指数对用户观察到的特征与参考指纹图中可用的特征之间的相似性进行量化;适应性特征选择采用适应性前向的贪婪搜索,确定每个次区域与特定基于指纹的定位方法有关的特征组别;在使用机会信号对拟议次区域进行指纹识别和特征选择的经验性研究中,将在线阶段的处理时间减少大约10倍,而定位准确性没有显著下降;事实上,在两项研究中,循环错误的90%增加了7.5%,而在另一项研究中,我们甚至发现相应的循环错误减少了30%。