模糊空间下双标签指纹定位算法

doi:10.19665/j.issn1001-2400.2023.02.009

摘要/Abstract

摘要：

针对指纹定位中传统的空间划分方法所伴随的指纹点区归属识别困难和邻界匹配误判的问题,提出一种适用于区域中心识别和过渡双域判别的空间模糊划分方法,利用参考点类间距离与类内距离的差异度,衡量子区域边界的模糊度,保证实验场景定位开销最优化的同时,兼顾空间重叠划分优势,缓解子区域间绝对判别的负效应,提高定位匹配的泛化能力。在位置估计阶段,综合考虑参考点邻域间接收信号波动差异,将参考点与待定位点间信号域的距离度量转化为同源差异下的无量纲排序,并以修正后的多源排序均衡结果间接映射待定位点与参考点之间的相似度;此外,引入空间密度可达搜索强相关参考点集,结合信号域和空间域迭代约束参考点,实现目标近邻集的动态选择和集聚效应,有效克服环境变化与信号波动的干扰,提高定位方法的环境适应性。经路演下的实测数据对定位性能的评估,所提算法的定位精度优于同类区划分算法4.7%~11.8%,且在同全局匹配方法的比较中,平均定位误差最佳可降低0.422 m。

关键词: 室内定位, 指纹定位, 模糊空间, 迭代约束

Abstract:

To address the problems of difficult identification of zone attribution of fingerprint points and misjudgment of neighboring zone matching accompanying the traditional spatial division method in fingerprint localization,a spatial fuzzy division method applicable to zone center identification and transition dual domain discrimination is proposed.By using the difference degree between inter-class distance and intra-class distance of reference points to measure the ambiguity of sub-region boundaries,we ensure the optimization of the localization cost of experimental scenes while taking into account the advantage of spatial overlap division,so as to alleviate the negative effect of absolute discrimination between sub-regions and improve the generalization ability of localization matching.In the position estimation stage,the distance metric in the signal domain between the reference point and the point to be located is transformed into a dimensionless ranking under the same source difference by considering the received signal fluctuation difference between the neighborhoods of the reference point,and the similarity between the point to be located and the reference point is indirectly mapped with the corrected multi-source ranking equalization result;in addition,the introduction of the spatial density reachable search strong correlation reference point set,combined with the signal domain and spatial domain iterative constraint reference points,to achieve dynamic selection and clustering effect of the target nearest neighbor set,so as to effectively overcome the interference of environmental changes and signal fluctuations,and improve the environmental adaptability of the localization method.After the evaluation of the localization performance by the measured data under the road it is shown that the proposed algorithm outperforms similar zoning algorithms in localization accuracy by 4.7%~11.8%,and that the average localization error can be best reduced by 0.422m in comparison with the global matching method.

Key words: indoor localization, fingerprint localization, fuzzy space, iterative constraint

中图分类号:

TN96

郑安琪, 秦宁宁. 模糊空间下双标签指纹定位算法[J]. 西安电子科技大学学报, 2023, 50(2): 81-91.

ZHENG Anqi, QIN Ningning. Dual-label fingerprint localization algorithm in fuzzy space[J]. Journal of Xidian University, 2023, 50(2): 81-91.

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子区域Ω_t	领域半径ε	领域阈值η	最小平均误差/m	待定位点T_P	近邻数K
Ω₂	4.5	7	0.973	(43.5,0.5) (51.5,0.5)	7 8
Ω₅	2.5	5	1.199	(27.5,6.5) (27.5,9.5)	5 6
Ω₇	1.5	7	1.102	(0.5,37.5) (3.5,38.5)	10 10

参考点排序修正/K值自适应	平均值/m	最小值/m	最大值/m	方差值/m	1 m以内误差占比/%
无/无	1.327	0.006	6.720	1.045	46.22
无/有	1.266	0.079	6.391	1.001	52.70
有/无	1.201	0.005	4.987	0.827	48.11
有/有	1.138	0.004	4.888	0.774	55.68

匹配形式	定位算法	平均误差	最小误差	最大误差	误差方差
	WKNN	1.560	0.007	5.756	1.328
全局匹配	EWKNN	1.552	0.017	6.588	1.507
	AAS	1.285	0.079	6.389	0.988
	APC	1.256	0.112	4.987	0.944
局部匹配	FCM	1.191	0.104	4.888	0.821
	k-means	1.185	0.006	4.888	0.809
	DLFS	1.138	0.004	4.888	0.774