Double Stage Indoor Localization Algorithm Based on LANDMARC and Compressive Sensing

LI Lina; MA Jun; LONG Yue; XU Panfeng

doi:10.11999/JEIT151050

Volume 38 Issue 7

Jul. 2016

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Article Navigation > Journal of Electronics & Information Technology > 2016 > 38(7): 1631-1637

LI Lina, MA Jun, LONG Yue, XU Panfeng. Double Stage Indoor Localization Algorithm Based on LANDMARC and Compressive Sensing[J]. Journal of Electronics & Information Technology, 2016, 38(7): 1631-1637. doi: 10.11999/JEIT151050

Citation:

LI Lina, MA Jun, LONG Yue, XU Panfeng. Double Stage Indoor Localization Algorithm Based on LANDMARC and Compressive Sensing[J]. Journal of Electronics & Information Technology, 2016, 38(7): 1631-1637. doi: 10.11999/JEIT151050

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Double Stage Indoor Localization Algorithm Based on LANDMARC and Compressive Sensing

doi: 10.11999/JEIT151050

1.
(College of Physics, Liaoning University, Shenyang 110036, China)
2.

Funds:

The National Natural Science Foundation of China (61403176), Science and Technology Research Project of Educational Commission of Liaoning Province of China (L2013003)

Received Date: 2015-09-17
Rev Recd Date: 2016-03-07
Publish Date: 2016-07-19

Abstract

Abstract

In consideration of the contradiction between the positioning accuracy and computational efficiency of the previous indoor positioning algorithm, a double stage positioning algorithm (LANDMARC- SROMP CS) using LANDMARC combined with Compressive Sensing based on the Regularized Orthogonal Matching Pursuit optimized by the Simulated annealing algorithm (SROMP) is put forward. First of all, LANDMARC location algorithm is used to lock the target area quickly; then in the locked area, Compressive Sensing (CS) theory is introduced to realize the target position estimation. In this part, firstly, the virtual reference tags are constructed according to the scale of the locked area; then, the measurement matrix is constructed by the received signal strength data of the virtual reference tags, and the signal strength data are calculated by the indoor propagation loss model which is trained by a new relevance vector machine algorithm based on mixed kernel functions. At last, the SROMP compressive sensing reconstruction algorithm is used to get the position index matrix, and the position information of the target also can be obtained through a simple weighted average calculation. The experimental results show that the average positioning error of the proposed algorithm is only 0.6445 m, and the computation efficiency of the proposed algorithm is relatively high, which can meet the indoor positioning requirements well.
- Indoor localization,
- Compressive Sensing (SC),
- Simulated annealing,
- Regularized Orthogonal Matching Pursuit (ROMP),
- Relevance Vector Machine (RVM)

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References

YANG Ting and ZHU Liping. A review of modern indoor localization systems[C]. Proceedings of the 2nd National Conference on Information Technology and Computer Science, Shanghai, 2015: 10-11.

WU Ling, and HUANG Liya. Improvement of location methods based on RFID[J]. The Journal of China Universities of Posts and Telecommunications, 2013, 20(6): 36-41.

LI Chengtie, WANG Jinkuan, and HAN Yinghua. An efficient compressed sensing-based cross-layer congestion control scheme for wireless sensor networks[C]. Proceedings of IEEE 26th Chinese Control and Decision conference, Shanghai, 2014: 5-6.

何风行, 余志军, 刘海涛. 基于压缩感知的无线传感器网络多目标定位算法[J]. 电子与信息学报, 2012, 34(3): 716-721. doi: 10.3724/SP.J.1146.2011.00405.

HE Fenghang, YU Zhijun, and LIU Haitao. Multiple target localization via compressed sensing in wireless sensor networks[J]. Journal of Electronics Information Technology, 2012, 34(3): 716-721. doi: 10.3724/SP.J.1146.2011.00405.

MIR Y U, KOPPARAPU V R, and YAND Dongkai. An enhanced K-nearest neighbor algorithm for indoor positioning systems in a WLAN[C]. Proceedings of 2014 IEEE Computers, Communications and IT Applications Conference (ComComAp), Beijing, 2014: 5-8.

ZHANG Rongbiao, GUO Jianguang, CHU Fuhuan, et al. Environmental-adaptive indoor radio path loss model for wireless sensor networks localization[J]. AEU - International Journal of Electronics and Communications, 2011, 65(12): 1023-1031.

MIHALIS A N, HATICE G, and MAJA P. Output- associative RVM regression for dimensional and continuous emotion prediction[J]. Image and Vision Computing, 2012, 30(3): 186-196.

赵春晖, 张燚, 王玉磊. 基于小波核主成分分析的相关向量机高光谱图像分类[J]. 电子与信息学报, 2012, 34(8): 29-32. doi: 10.3724/SP.J.1146.2011.01282.

ZHAO Chunhui , ZHANG Yi, and WANG Yulei. Relevant vector machine classification of hyperspectral image based on wavelet kernel principal component analysis[J]. Journal of Electronics Information Technology, 2012, 34(8): 29-32. doi: 10.3724/SP.J.1146.2011.01282.

NURCIHAN C. Application of support vector machines and relevance vector machines in predicting uniaxial compressive strength of volcanic rocks[J]. Journal of African Earth Sciences, 2014, 100(11): 634-644.

CLMENT D and FRDRIC E M. Stationary max-stable processes with the Markov property[J]. Stochastic Processes and their Applications, 2014, 124(6): 2266-2279.

唐朝伟, 李超群, 燕凯, 等. 基于LISOMAP的相关向量机入侵检测模型[J]. 计算机应用, 2012, 32(9): 2606-2608.

TANG Chaowei, LI Chaoqun, YAN Kai, et al. Intrusion detection model based on LISOMAP relevant vector machine[J]. Journal of Computer Applications, 2012, 32(9): 2606-2608.

WANG Li, HU Jianfeng, and LIU Yongwei. Proceedings of an adaptive line search scheme for compressed sensing based on nemirovski's algorithm[C]. Proceedings of the 2nd National Conference on Information Technology and Computer Science, Shanghai, 2015: 7-8.

GHAEDI M, GHAEDI A M, HOSSAINPOUR M, et al. Least square-support vector (LS-SVM) method for modeling of methylene blue dye adsorption using copper oxide loaded on activated carbon: Kinetic and isotherm study[J]. Journal of Industrial and Engineering Chemistry, 2014, 25(4): 1641-1649.

李蕴华. 压缩感知框架下基于ROMP 算法的图像精确重构[J]. 计算机应用, 2011, 31(10): 2714-2716.

LI Yunhua. Precise image reconstruction based on ROMP algorithm in compressive sensing[J]. Journal of Computer Applications, 2011, 31(10): 2714-2716.

LIN C K Y, Haley K B, and SPARKS C. A comparative study of both standard and adaptive versions of threshold accepting and simulated annealing algorithms in three scheduling problems[J]. European Journal of Operational Research, 1995, 83(2): 330-346.

OLG A S P, EFSTATHIOS Z, FADY R M, et al. Sensory and microbiological quality assessment of beef fillets using a portable electronic nose in tandem with support vector machine analysis[J]. Food Research International, 2013, 50(1): 241-249.

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