信号指纹测量下双度量协同的室内定位方法

Indoor localization method based on dual-metric coordination of signal fingerprint measurement

针对室内WiFi定位中指纹信息冗余、空间边界划分困难和RP集获取精准度缺失的问题,提出一种信号指纹测量下双度量协同的室内定位方法。通过S度量和欧氏度量下指纹矩阵融合,精简形成低维指纹信息,考量指纹间“点-类”关联度和“类-类”相似度,兼顾子区域边界新增指纹数目的可控性,确立子区域边界模糊深度调整机制,形成边界模糊泛化能力,以区域稀疏度判定插值方法完成指纹库扩充,以构建高密度离线指纹库。在优选子区域中,结合信号空间和位置空间,对比两类度量的差异度,实现对高价值指纹点的定向筛选,削弱在线指纹匹配集合的误差影响。在全局实验场景中,分区结果规整有序,较为符合实际空间构造。指纹库构建效果较其他方案至少提升11%,定位精度相对同类型算法提升了12%以上,论文所提方案定位精度优势显著,在具备高扰动特点下的复杂室内环境中,具有较好的场景适应性。

To address the problems of fingerprint information redundancy,difficulty in spatial boundary division,and lack of accuracy in acquiring RP sets in indoor WiFi localization,we propose an indoor localization method based on dual-metric coordination of signal fingerprint measurement.The low-dimensional fingerprint information is simplified and formed through the fusion of fingerprint matrix under S-metric and European-metric.The correlation degree between fingerprints is considered through the“point-class”correlation degree and“class-class”similarity,taking into account the controllability of the number of new fingerprints on the subregion boundary,and the adjustment mechanism of the fuzzy depth of the subregion boundary is established to form the boundary ambiguity generalization ability.Expansion of the fingerprint database is accomplished by the interpolation method of regional sparsity determination,so as to construct a high-density offline fingerprint database.In the preferred subregion,combining the signal space and the location space,the difference degree of the two kinds of measurements is compared to realize the targeted screening of high-value fingerprint points,and reduce the error influence of online fingerprint matching set.In the global experimental scene,the partition results are regular and orderly,which accords with the actual space structure.The construction effect of fingerprint database is improved by at least 11%compared with other schemes,and the positioning accuracy is improved by more than 12%compared with the same type of algorithm.The proposed scheme has significant positioning accuracy advantages,and has better scene adaptability in complex indoor environments with high disturbance characteristics.