模糊空间下双标签指纹定位算法

doi:10.19665/j.issn1001-2400.2023.02.009

Abstract

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

CLC Number:

TN96

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

Figures/Tables 10

References 18

[1]	ZHOU M, LI Y, MUHAMMAD J, et al. Integrated Statistical Test of Signal Distributions and Access Point Contributions for Wi-Fi Indoor Localization[J]. IEEE Transactions on Vehicular Technology, 2021, 70(5):5057-5070. doi: 10.1109/TVT.2021.3076269
[2]	秦宁宁, 吴忆松, 孙顺远. 一种双信标机制下的指纹库构建方法[J]. 西安电子科技大学学报, 2022, 49(3):111-119.
	QIN Ningning, WU Yisong, SUN Shunyuan. Algorithm for Fingerprint Database Construction Under the Dual Beacon Mechanism[J]. Journal of Xidian University, 2022, 49(3):111-119.
[3]	BAI S, WU T. Analysis of K-MeansAlgorithm on Fingerprint Based Indoor Localization System[C]// 2013 5th IEEE International Symposium on Microwave,Antenna, Propagation and EMC Technologies for Wireless Communications.Piscataway:IEEE, 2013:44-48.
[4]	ALTINTAS B, SERIF T. Improving RSS-Based Indoor Positioning Algorithm Via K-Means Clustering[C]// Proceedings of the European Wireless.Piscataway:IEEE 2011:681-685.
[5]	ZHOU H, VAN N. Indoor Fingerprint Localization Based on Fuzzy C-means Clustering[C]//Proceedings of the 2014 Sixth International Conference on Measuring Technology and Mechatronics Automation. Piscataway:IEEE, 2014:337-340.
[6]	TIAN Z, TANG X, ZHOU M, et al. Fingerprint Indoor Positioning Algorithm Based on Affinity Propagation Clustering[J]. EURASIP Journal on Wireless Communications and Networking, 2013(1):1-8.
[7]	YANG H, ZHANG Y, HUANG Y, et al. WKNN Indoor Location Algorithm Based on Zone Partition by Spatial Features and Restriction of Former Location[J]. Pervasive and Mobile Computing, 2019, 60(10):1-14.
[8]	HU J, LIU D, YAN Z, et al. Experimental Analysis on Weight K-nearest Neighbor Indoor Fingerprint Positioning[J]. IEEE Internet of Things Journal, 2019, 6(1):891-897. doi: 10.1109/JIOT.2018.2864607
[9]	ZHANG W, YU K, WANG W, et al. A Self-adaptive Ap Selection Algorithm Based on Multiobjective Optimization for Indoor WiFi Positioning[J]. IEEE Internet of Things Journal, 2020, 8(3):1406-1416. doi: 10.1109/JIoT.6488907
[10]	TAO Y, ZHAO L. Fingerprint Localization with Adaptive Area Search[J]. IEEE Communications Letters, 2020, 24(7):1446-1450. doi: 10.1109/COML.4234
[11]	王培重, 郑南山, 张言哲. 基于动态K 值及AP MAC 地址筛选的室内定位算法[J]. 计算机科学, 2016, 43(1):163-165. doi: 10.11896/j.issn.1002-137X.2016.01.037
	WANG Peizhong, ZHENG Nanshan, ZHANG Yanzhe. Indoor Positioning Algorithm Based on Dynamic K Value and AP MAC Address Match[J]. Computer Science, 2016, 43(1):163-165. doi: 10.11896/j.issn.1002-137X.2016.01.037
[12]	TAO Y, ZHAO L. AIPS:An Accurate Indoor Positioning System with Fingerprint Map Adaptation[J]. IEEE Internet of Things Journal, 2022, 9(4):3062-3073. doi: 10.1109/JIOT.2021.3095185
[13]	谢娟英, 周颖, 王明钊, 等. 聚类有效性评价新指标[J]. 智能系统学报, 2017, 12(6):873-882.
	XIE Juanying, ZHOU Ying, WANG Mingzhao, et al. New Criteria for Evaluating the Validity of Clustering[J]. CAAI Transactions on Intelligent Systems, 2017, 12(6):873-882.
[14]	YIN J, YANG Q, NI L. Learning Adaptive Temporal Radio Maps for Signal-Strength-Based Location Estimation[J]. IEEE Transactions on Mobile Computing, 2008, 7(7):869-883. doi: 10.1109/TMC.2007.70764
[15]	DOVOEDO Y H, CHAKRABORTI S. Boxplot-Based Outlier Detection for the Location-Scale Family[J]. Communications in Statistics Simulation and Computation, 2015, 44(6):1492-1513. doi: 10.1080/03610918.2013.813037
[16]	HE S, LIN W, CHAN S H G. Indoor Localization and Automatic Fingerprint Update with Altered AP Signals[J]. IEEE Transactions on Mobile Computing, 2016, 16(7):1897-1910. doi: 10.1109/TMC.2016.2608946
[17]	万佳, 胡大裟, 蒋玉明. 多密度自适应确定DBSCAN算法参数的算法研究[J]. 计算机工程与应用, 2022, 58(2):78-85. doi: 10.3778/j.issn.1002-8331.2012-0476
	WAN Jia, HU Dasha, JIANG Yuming. Research on Method of Multi-Density Self-Adaptive Determination of DBSCAN Algorithm Parameters[J]. Computer Engineering and Applications, 2022, 58(2):78-85. doi: 10.3778/j.issn.1002-8331.2012-0476
[18]	MA L, ZHANG L, QIN D. A Novel Indoor Fingerprint Localization System Based on Distance Metric Learning and AP Selection[J]. IEEE Transactions on Instrumentation and Measurement, 2022, 71(1):1-15.

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

Dual-label fingerprint localization algorithm in fuzzy space

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