基于有效AP选择和多分类LDA的室内定位算法

doi:10.11959/j.issn.1000-436x.2021211

Abstract

Abstract:

To solve the problems of the lack of longitudinal position information in indoor positioning, an indoor positioning algorithm based on effective AP selection and multi-class LDA was proposed.In the offline stage, the AP selection algorithm based on stability and difference was used to extract effective AP set in the fingerprint area, and the multi-class LDA algorithm was adopted to train the fingerprint data of effective AP set at different heights to obtain longitudinal position information recognition model.In the online stage, the AP information alike the offline effective AP subset was input into longitudinal position information recognition model to obtain recognition result, and then the online fingerprint plane positioning was completed by the positioning algorithm based on the fingerprint database partition.Simulation results demonstrate that the accuracy of the proposed algorithm for determining longitudinal floors can reach 98%.

Key words: indoor localization, fingerprinting, longitudinal position information recognition, access point selection, LDA

CLC Number:

TN92

Guiqi LIU, Zhihong QIAN, Hualiang LI, Jiani SUN, Yinuo FENG, Xue WANG. Indoor positioning algorithm based on effective AP selection and multi-class LDA[J]. Journal on Communications, 2021, 42(11): 109-120.

Figures/Tables 16

参数	数值
rss_thr	${median(rss}_{{ap}_{1}} {,rss}_{{ap}_{2}}, ... {,rss}_{ap}_{_{m}})$
rss_r,th	${median(rss}_{ap}_{_{1}} {,rss}_{ap}_{_{2}}, ... {,rss}_{ap}_{_{m}})$
α	0.75
m_ap	5
m'	8

References 16

[1]	KHALAJMEHRABADI A , GATSIS N , AKOPIAN D . Modern WLAN fingerprinting indoor positioning methods and deployment challenges[J]. IEEE Communications Surveys ＆ Tutorials, 2017,19(3): 1974-2002.
[2]	SHIT R C , SHARMA S , PUTHAL D ,et al. Location of things (LoT):a review and taxonomy of sensors localization in IoT infrastructure[J]. IEEE Communications Surveys ＆ Tutorials, 2018,20(3): 2028-2061.
[3]	HAVYARIMANA V , HANYURWIMFURA D , NSENGIYUMVA P ,et al. A novel hybrid approach based-SRG model for vehicle position prediction in multi-GPS outage conditions[J]. Information Fusion, 2018,41: 1-8.
[4]	XIAO Z , LI P T , HAVYARIMANA V ,et al. GOI:a novel design for vehicle positioning and trajectory prediction under urban environments[J]. IEEE Sensors Journal, 2018,18(13): 5586-5594.
[5]	钱志鸿, 孙大洋, LEUNG Victor, . 无线网络定位综述[J]. 计算机学报, 2016,39(6): 1237-1256.
	QIAN Z H , SUN D Y , LEUNG V . A survey on localization model in wireless networks[J]. Chinese Journal of Computers, 2016,39(6): 1237-1256.
[6]	李华亮, 钱志鸿, 田洪亮 . 基于核函数特征提取的室内定位算法研究[J]. 通信学报, 2017,38(1): 158-167.
	LI H L , QIAN Z H , TIAN H L . Research on indoor localization algorithm based on kernel principal component analysis[J]. Journal on Communications, 2017,38(1): 158-167.
[7]	WU C S , YANG Z , XIAO C W . Automatic radio map adaptation for indoor localization using smartphones[J]. IEEE Transactions on Mobile Computing, 2018,17(3): 517-528.
[8]	LI H L , QIAN Z H , TIAN C S ,et al. TILoc:improving the robustness and accuracy for fingerprint-based indoor localization[J]. IEEE Internet of Things Journal, 2020,7(4): 3053-3066.
[9]	CRAMARIUC A , HUTTUNEN H , LOHAN E S . Clustering benefits in mobile-centric Wi-Fi positioning in multi-floor buildings[C]// 2016 International Conference on Localization and GNSS (ICL-GNSS). Piscataway:IEEE Press, 2016: 1-6.
[10]	RADU V , MARINA M K . HiMLoc:indoor smartphone localization via activity aware pedestrian dead reckoning with selective crowdsourced Wi-Fi fingerprinting[C]// International Conference on Indoor Positioning and Indoor Navigation. Piscataway:IEEE Press, 2013: 1-10.
[11]	周牧, 王斌, 田增山 ,等. 室内BLE/MEMS跨楼层融合定位算法[J]. 通信学报, 2017,38(5): 1-10.
	ZHOU M , WANG B , TIAN Z S ,et al. Indoor BLE and MEMS based multi-floor fusion positioning algorithm[J]. Journal on Communications, 2017,38(5): 1-10.
[12]	SUN L , ZHENG Z W , HE T ,et al. Multifloor Wi-Fi localization system with floor identification[J]. International Journal of Distributed Sensor Networks, 2015,11(7): 131523.
[13]	LUO J , ZHANG Z Y , WANG C ,et al. Indoor multifloor localization method based on Wi-Fi fingerprints and LDA[J]. IEEE Transactions on Industrial Informatics, 2019,15(9): 5225-5234.
[14]	LIU G Q , QIAN Z H , WANG X . An indoor WLAN location algorithm based on fingerprint database processing[J]. International Journal of Pattern Recognition and Artificial Intelligence, 2020,34(10): 2050026.
[15]	CLUNIES-ROSS C W , RIFFENBURGH R H . Geometry and linear discrimination[J]. Biometrika, 1960,47(1/2): 185-189.
[16]	TORRES-SOSPEDRA J , MONTOLIU R , MARTíNEZ-USó A ,et al. UJIIndoorLoc:a new multi-building and multi-floor database for WLAN fingerprint-based indoor localization problems[C]// 2014 International Conference on Indoor Positioning and Indoor Navigation (IPIN). Piscataway:IEEE Press, 2014: 261-270.

Metrics

Recommended 0

No Suggested Reading articles found!

建筑物	F	N_f	N_t	N_ap
T₀	4	5 249	536	200
T₁	4	5 196	307	207
T₂	5	9 492	268	203

楼层	T₀	T₁	T₂
第一层	99%	98%	98%
第二层	97%	97%	96%
第三层	97%	98%	96%
第四层	98%	98%	97%
第五层	—	—	98%
平均	98%	98%	97%

参考点	161	51	162	52	25
1	-100	-100	-100	-100	-100
2	-95	-100	-94	-100	-80
3	-71	-87	-71	-88	-92
4	-65	-77	-71	-77	-100
5	-85	-100	-83	-100	-91
6	-87	-100	-88	-100	-89

参考点	第一维	第二维	第三维
1	66.1492	78.4905	0.1239
2	48.1170	65.7072	-0.2531
3	50.1185	80.3818	-0.1433
4	61.7909	84.0203	3.8362
5	48.9408	79.0238	-1.0936
6	50.0295	76.5652	-0.9758

楼层	均值	方差
第一层	(61.7,79.2,-0.099)	(39.8, 43.1, 7.35)
第二层	(55.8,79.5,0.117)	(99.7,124.0, 2.16)
第三层	(65.2,76.1,0.313)	(58.0,46.2,11.73)
第四层	(74.9,71.4,0.045)	(89.5,49.0,9.12)

Indoor positioning algorithm based on effective AP selection and multi-class LDA

RichHTML

PDF

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 16

References 16

Related Articles 11

Metrics

Recommended 0

算法	T₀	T₁	T₂
k-means算法	0.84	0.83	0.78
PLGD	0.90	0.91	0.89
MA-LDA	0.86	0.85	0.81
EAP-LDA	0.98	0.98	0.97

[1]	Zenghua ZHAO, Yuefan TONG, Jiayang CUI. Device-independent Wi-Fi fingerprinting indoor localization model based on domain adaptation [J]. Journal on Communications, 2022, 43(4): 143-153.
[2]	Liangbo XIE, Xixi LIU, Yong WANG, Mu ZHOU, Zengshan TIAN. Indoor EKF localization algorithm based on RFID carrier phase [J]. Journal on Communications, 2022, 43(3): 124-134.
[3]	Liangbo XIE, Sheng LI, Mu ZHOU, Ze LI, Zengshan TIAN, Ya WANG, Changyou FU. Scatterer information based indoor NLOS multiple base station cooperative localization algorithm [J]. Journal on Communications, 2021, 42(5): 63-74.
[4]	Mu ZHOU, Yuexin LONG, Qiaolin PU, Yong WANG, Wei HE. Indoor Wi-Fi fingerprint localization method based on CSI tensor decomposition [J]. Journal on Communications, 2021, 42(11): 159-171.
[5]	Yanfen LE,Zhuo TANG,Cunbao SHENG,Weibin SHI. Fast and resource efficient method for indoor localization based on fingerprint with varied scales [J]. Journal on Communications, 2019, 40(1): 172-179.
[6]	Hua-liang LI,Zhi-hong QIAN,Hong-liang TIAN. Research on indoor localization algorithm based on kernel principal component analysis [J]. Journal on Communications, 2017, 38(1): 158-167.
[7]	Mu ZHOU,Qiao-lin PU,Zeng-shan TIAN. Location fingerprint optimization based access point deployment in indoor WLAN localization [J]. Journal on Communications, 2015, 36(Z1): 30-41.
[8]	Ting-ting WANG,Wei KE,Chao SUN. Environmental-adaptive RSS-based indoor localization [J]. Journal on Communications, 2014, 35(10): 210-217.
[9]	. Environmental-adaptive RSS-based indoor localization [J]. Journal on Communications, 2014, 35(10): 24-217.
[10]	Jun-ying GAN,Yong-hui KUANG,Juan XIAO. Face recognition based on IMED-Isomap and direct LDA [J]. Journal on Communications, 2011, 32(9A): 54-58.
[11]	Li-qiu TAN,Yan-bo HUANG,Yao-ping FEI. Using RBAC model in access control of campus information portal [J]. Journal on Communications, 2005, 26(1A): 178-181.