自适应环境变化的RSS室内定位方法

doi:10.3969/j.issn.1000-436x.2014.10.024

Abstract

Abstract:

A novel two-step dictionary learning (DL) framework was proposed to dynamically adjust the overcomplete basis (a.k.a.dictionary) for matching the changes of the RSS measurements,and then the sparse solution can better represent location estimations.Moreover,a modified re-weighting l₁norm minimization algorithm was proposed to improve reconstruction performance for sparse signals.The effectiveness of the proposed scheme is demonstrated by experimental results where the locations of targets can be obtained from noisy signals,even if the number of targets is not known a priori.

Key words: indoor localization, dictionary learning (DL), compressive sensing

Ting-ting WANG,Wei KE,Chao SUN. Environmental-adaptive RSS-based indoor localization[J]. Journal on Communications, 2014, 35(10): 210-217.

Figures/Tables 9

References 13

[14]	TROP J A , GILBERT A C . Signal recovery from random measurements via orthogonal matching pursuit[J]. IEEE Transactions on Information Theory, 2007,53(12): 4655-4666.
[15]	CHEN S S , DONOHO D L , SAUNDERS M A . Atomic decomposition by basis pursuit[J]. SIAM Review, 2001,43(1): 129-159.
[16]	CANDES E J , WAKIN M B . An introduction to compressive sampling[J]. IEEE Signal Processing Magazine, 2008,25(2): 21-30.
[17]	CANDES E J , WAKIN M B , and BOYD S P . Enhancing sparsity by reweighted l1minimization[J]. Journal of Fourier Analysis Application, 2008,14(5/6): 877-905.
[1]	宋震龙, 蒋刚毅, 黄晁 ,等. 基于偏度?峰度检验的无线局域网室内定位算法[J]. 通信学报, 2012,33(5): 99-105. SONG Z L , JIANG G Y , HUANG C ,et al. WLAN indoor positioning algorithm based on skewness-kurtosis testing[J]. Journal on Communications, 2012,33(5): 99-105.
[2]	LIU H , DARABI H , BANERJEE H,et.al . Survey of wireless indoor positioning techniques and systems[J]. IEEE Transactions on Systems,Man,and Cybernetics-Part C, 2007,37(6): 1067-1080.
[3]	CEVHER V , DUARTE M F , BARANIUK R G . Distributed target localization via spatial sparsity[A]. EUSIPCO[C]. 2008. 25-29.
[4]	CEVHER V , BOUFOUNOS P , BARANIUK R G ,et al. Near-optimal bayesian localization via incoherence and sparsity[A]. IPSN[C]. 2009. 205-216.
[5]	FENG C,AU W S A , VALAEE S , TAN Z . Received-signal-strengthbased indoor positioning using compressive sensing[J]. IEEE Transactions on Mobile Computing, 2012,11(12): 1983-1993.
[6]	AU W S A , FENG C , VALAEE S,et.al . Indoor tracking and navigation using received signal strength and compressive sensing on a mobile device[J]. IEEE Transactions on Mobile Computing, 2013,12(10): 2050-2062.
[7]	CUI Q , DENG J , ZHANG X . Compressive sensing based wireless localization in indoor scenarios[J]. China Communications, 2012,19(4): 1-12.
[8]	DENG J , CUI Q , ZHANG X,et.al . Compressive sensing based indoor positioning with denosing and filtering in LF space[A]. PIMRC[C]. 2012. 2477-2482.
[9]	ZHANG B , CHENG X , ZHANG N . Sparse target counting and localization in sensor networks based on compressive sensing[A]. IEEE INFOCOM[C]. 2011. 2255-2263.
[10]	KAMOL K , PRASHANT K . Properties of indoor received signal strength for WLAN location fingerprinting[A]. First Annual International Mobile and Ubiquitous Systems Conference[C]. 2004. 14-23.
[11]	RAPPAPORT T S . Wireless Communication:Principles and Practice[M]. Englewood Cliffs,NJ: Prentice-HallPress, 1999.
[12]	RUBINSTEIN R , BRUCKSTEIN A M and Elad M . Dictionaries for sparse representation modeling[J]. Proc of IEEE, 2010,98(6): 1045-1057.
[18]	MAIRAL J , BACH F , PONCE J ,et al. Online learning for matrix factorization and sparse coding[J]. J Mach Learn Res, 2010,11(3): 19-60.
[19]	朱辉 . 实用射频测试与测量[M]. 北京: 电子工业出版社, 2012. ZHU H . Practical RF Test and Measurement[M]. Beijing: Electronic Industry PressPress, 2012,China.
[13]	NOCEDAL J , WRIGHT S J . Numerical Optimization[M]. New York: Springer VerlagPress, 2006.

Metrics

Recommended 0

No Suggested Reading articles found!

算法	RMSE/m	失效率/%	平均耗时/s
对比算法1 s=3 m²	3.91	9	0.610
对比算法2 s=3 m²	4.35	16	0.325
本文算法 s=3 m²	2.77	8	1.211(离线)+0.750(在线)
对比算法1 s=1 m²	2.03	7	0.876
对比算法2 s=1 m²	2.81	12	0.551
本文算法 s=1 m²	1.51	6	2.957(离线)+0.997(在线)

Environmental-adaptive RSS-based indoor localization

RichHTML

PDF

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 9

References 13

Related Articles 15

Metrics

Recommended 0

[1]	Hua REN, Shaozhang NIU, Ruyong REN, Zhen YUE. Research on meaningful image encryption algorithm based on 2-dimensional compressive sensing [J]. Journal on Communications, 2022, 43(5): 45-57.
[2]	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.
[3]	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.
[4]	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.
[5]	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.
[6]	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.
[7]	Weina YUAN,Qiu YAN. Channel estimation method based on compressive sensing for FBMC/OQAM system [J]. Journal on Communications, 2019, 40(12): 98-104.
[8]	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.
[9]	Feiyun WU,Kunde YANG,Feng TONG. Partial-norm-constrained sparse recovery algorithm and its application on single carrier underwater-acoustic-data telemetry [J]. Journal on Communications, 2018, 39(6): 127-132.
[10]	Ce ZHANG,Ou LI,Xin TONG,Yanping YANG. WSN data gathering algorithm based on compressive sensing and matrix completion technique [J]. Journal on Communications, 2018, 39(2): 164-173.
[11]	Xin PENG,Ren-fa LI,Zhe-tao LI,Guo-qi XIE. Data dissemination of VANET based on lower bound of road delay [J]. Journal on Communications, 2017, 38(4): 25-34.
[12]	Peng SUN,Gui-nan LI,Lian-tao WU,Zhi WANG. Sparse signal transmission under lossy wireless links based on double process of compressive sensing [J]. Journal on Communications, 2017, 38(4): 120-128.
[13]	Li-ye PEI,Hua JIANG,Yue-liang MA. Denoising recovery for compressive sensing based on selective measure [J]. Journal on Communications, 2017, 38(2): 106-114.
[14]	Jia LI,Zhi-rong GAO,Cheng-yi XIONG,Cheng ZHOU. Image compressive sensing recovery based on weighted structure group sparse representation [J]. Journal on Communications, 2017, 38(2): 196-202.
[15]	Li-jun GE,Hui GUO,Yue LI,Lan ZHAO. Sparsity adaptive channel estimation algorithm based on compressive sensing for massive MIMO systems [J]. Journal on Communications, 2017, 38(12): 57-62.