Weighted semidefinite programming scheme for wireless positioning with lognormal shadowing

doi:10.19682/j.cnki.1005-8885.2023.0007

中国邮电高校学报(英文) ›› 2023, Vol. 30 ›› Issue (5): 93-100.doi: 10.19682/j.cnki.1005-8885.2023.0007

• Wireless • 上一篇

Weighted semidefinite programming scheme for wireless positioning with lognormal shadowing

田克冈,徐文波,王思野,陈湘森

北京邮电大学

收稿日期:2022-10-07 修回日期:2023-03-06 出版日期:2023-10-31 发布日期:2023-10-30
通讯作者: 徐文波 E-mail:xuwb@bupt.edu.cn
基金资助:
the National Natural Science Foundation of China (61871050).

Weighted semidefinite programming scheme for wireless positioning with lognormal shadowing

School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 10876, China

Received:2022-10-07 Revised:2023-03-06 Online:2023-10-31 Published:2023-10-30
Contact: Xu Wen-Bo E-mail:xuwb@bupt.edu.cn
Supported by:
the National Natural Science Foundation of China (61871050).

摘要/Abstract

摘要：

Received signal strength (RSS) based positioning schemes ignore the actual environmental feature that the volatility of RSS increases as signal propagation distance grows. Therefore, RSS over long distance generally has relatively large measurement error and degrades the positioning performance. To reduce the negative impact of these RSSs over long distances, a weighted semidefinite programming (WSDP) positioning scheme was proposed. The WSDP positioning scheme first assesses the signal propagation quality using the average variance of all RSS sets. Then appropriate weighting factors are set based on the variance of each RSS set, and a weighted semidefinite programming optimizer is formulated to estimate the positions of target nodes. Simulation results show that the WSDP positioning scheme can effectively improve the positioning performance.

关键词: 定位，接收信号强度，半正定规划，对数正态阴影

Abstract:

Key words: localization, received signal strength (RSS), semidefinite programming (SDP), lognormal shadowing

Tian Kegang, Xu Wenbo , Wang Siye, Chen Xiangsen. Weighted semidefinite programming scheme for wireless positioning with lognormal shadowing[J]. The Journal of China Universities of Posts and Telecommunications, 2023, 30(5): 93-100.

参考文献

[1] YOSHIDA K, SUGIYAMA T. Calculated distance error improvement by using drones in relay type GPS. Proceedings of the 2020 International Conference on Information and Communication Technology Convergence (ICTC'20), 2020, Oct 21 - 23, Jeju, Republic of Korea. Piscataway, NJ, USA: IEEE, 2020: 792 -794.

[2] LICHTENSTEIN M, ELKAIM G. Efficient GPS scheduling in wildlife tags using an extended Kalman filter-based uncertainty suppression strategy. Proceedings of the 2020 IEEE/ ION Position, Location and Navigation Symposium (PLANS'20), 2020 Apr 20 -

23, Portland, OR, USA. Piscataway, NJ, USA: IEEE, 2020: 1472 -1475.

[3] CHUNHAKAM P, PUMMARIN P, JEEN-IM P, et al. GPS positon predicting system by Kalman filter with velocity from OBD and direction from magnetometer. Proceedings of the 9th International Electrical Engineering Congress ( iEECON'21 ), 2021, Mar 10 - 12, Pattaya, Thailand, Piscataway, NJ, USA: IEEE, 2021: 444 -447.

[4] ZOU Y B, LIU H P. A simple and efficient iterative method for TOA localization. Proceedings of the 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP'20), 2020, May 4 -8, Barcelona, Spain. Piscataway, NJ, USA: IEEE, 2020: 4881 -4884.

[5] YAN Y S, YANG G, WANG H Y, et al. Semidefinite relaxation for source localization with quantized ToA measurements and transmission uncertainty in sensor networks. IEEE Transactions on Communications, 2021, 69(2): 1201 -1213.

[6] ZOU Y B, LIU H P. TDOA localization with unknown signal propagation speed and sensor position errors. IEEE Communications Letters, 2020, 24(5): 1024 -1027.

[7] ZOU Y B, LIU H P. Semidefinite programming methods for alleviating clock synchronization bias and sensor position errors in

TDOA localization. IEEE Signal Processing Letters, 2020, 27: 241 -245.

[8] GUI L Q, XIAO F, ZHOU Y, et al. Connectivity based DV-hop localization for Internet of things. IEEE Transactions on Vehicular

Technology, 2020, 69(8): 8949 -8958.

[9] LI Q, HUANG X, XU Y H, et al. Improved DV-hop based on dynamic parameters differential evolution localization algorithm. Proceedings of the IEEE 8th International Conference on Information, Communication and Networks ( ICICN'20), 2020, Aug 22 -25, Xi'an, China. Piscataway, NJ, USA: IEEE, 2020: 129 -134.

[10] ZHANG G C, ZHANG L H. WSN location algorithm based on efficient simulated annealing weighted DV-Hop. Proceedings of the

4th International Conference on Communication and Information Systems ( ICCIS'19 ), 2019, Dec 19 - 21, Wuhan, China. Piscataway, NJ, USA: IEEE, 2019: 113 -117.

[11] SIVASAKTHISELVAN S, NAGARAJAN V. Localization techniques of wireless sensor networks: a review. Proceedings of the 2020 International Conference on Communication and Signal Processing ( ICCSP'20), 2020, Jul 28 - 30, Chennai, India. Piscataway, NJ, USA: IEEE, 2020: 1643 -1648.

[12] JIN D, YIN F, FRITSCHE C, et al. Bayesian cooperative localization using received signal strength with unknown path loss exponent: message passing approaches. IEEE Transactions on Signal Processing, 2020, 68: 1120 -1135.

[13] SINHA S, ASHWINI S. RSSI based improved weighted centroid localization algorithm in WSN. Proceedings of the 2nd International Conference for Emerging Technology (INCET'21), 2021, May 21 - 23, Belagavi, India. Piscataway, NJ, USA: IEEE, 2021: 1 -4.

[14] KALLEL O, JEMAA Z B. RSSI based localization in realistic environment. Proceedings of the 4th International Conference on

Advanced Systems and Emergent Technologies ( IC-ASET'20), 2020, Dec 15 -18, Hammamet, Tunisia. Piscataway, NJ, USA: IEEE, 2020: 199 -202.

[15] XU L D. Enterprise systems: state-of-the-art and future trends. IEEE Transactions on Industrial Informatics, 2011, 7 ( 4 ): 630 -640.

[16] PATWARI N, HERO A O, PERKINS M, et al. Relative location estimation in wireless sensor networks. IEEE Transactions on Signal Processing, 2003, 51(8): 2137 -2148.

[17] TOMIC S, BEKO M, DINIS R. RSS-based localization in wireless sensor networks using convex relaxation: noncooperative and

cooperative schemes. IEEE Transactions on Vehicular Technology, 2015, 64(5): 2037 -2050.

[18] OUYANG R W, WONG A K S, LEA C T. Received signal strength-based wireless localization via semidefinite programming: noncooperative and cooperative schemes. IEEE Transactions on Vehicular Technology, 2010, 59(3): 1307 -1318.

[19] BOYD S P, VANDENBERGHE L. Convex optimization. Cambridge, UK: Cambridge University Press, 2004.

[20] XIANG M T, LI L H, LONG C Z, et al. A new localization algorithm for wireless sensor network. Proceedings of the 2011 International Conference on Electrical and Control Engineering, 2011, Sep 16 - 18, Yichang, China. Piscataway, NJ, USA: IEEE, 2011: 513 -516.

[21] DU T X, WANG Z. An improved bearing-only based semidefinite programming algorithm for large scale WSN node localization.

Proceedings of the 9th IEEE Conference on Industrial Electronics and Applications, 2014, Jun 9 - 11, Hangzhou, China. Piscataway, NJ, USA: IEEE, 2014: 504 -509.

[22] LI S C, WANG X H, ZHAO S S, et al. Local semidefinite programming-based node localization system for wireless sensor network applications. IEEE Systems Journal, 2014, 8 ( 3 ): 879 -888.

[23] GUO X S, CHU L, SUN X. Accurate localization of multiple sources using semidefinite programming based on incomplete range

matrix. IEEE Sensors Journal, 2016, 16(13): 5319 -5324.

[24] DENG L J, WEI P, ZHANG H G. A semidefinite programming approach for target localization in the presence of sensors location

errors. Proceedings of the 3rd IEEE International Conference on Computer and Communications (ICCC'17), 2017, Dec 13 - 16, Chengdu, China. Piscataway, NJ, USA: IEEE, 2017: 882 -886.

[25] WANG Z F, ZHANG H, LU T T, et al. Cooperative RSS-based localization in wireless sensor networks using relative error estimation and semidefinite programming. IEEE Transactions on Vehicular Technology, 2019, 68(1): 483 -497.

[26] WU X P, QI H N, XIONG N X. Rank-one semidefinite programming solutions for mobile source localization in sensor networks. IEEE Transactions on Network Science and Engineering, 2021, 8(1): 638 -650.

[27] AL-DHALAAN A H, LAMBADARIS I. Semidefinite programming for wireless sensor localization with lognormal shadowing. Proceedings of the 3rd International Conference on Sensor Technologies and Applications, 2009, Jun 18 - 23, Athens, Greece. Piscataway, NJ, USA: IEEE, 2009: 187 -193.

[28] MANKONG T, MAW M M, PROMWONG S. 5. 8 GHz wireless localization based weighted algorithm for home network applications. Proceedings of the 6th International Conference on Engineering, Applied Sciences and Technology ( ICEAST'20), 2020, Jul 1 - 4, Chiang Mai, Thailand. Piscataway, NJ, USA: IEEE, 2020: 1 -4.

[29] POULOSE A, HAN D S. Indoor localization using PDR with Wi-Fi weighted path loss algorithm. Proceedings of the 2019 International Conference on Information and Communication Technology Convergence (ICTC'19), 2019, Oct 16 - 18, Jeju, Republic of Korea. Piscataway, NJ, USA: IEEE, 2019: 689 -693.

[30] THONGKAM J, SUPANAKOON P, PROMWONG S. Indoor wireless sensor network localization using RSSI based weighting algorithm method for short range wireless communication.

Proceedings of the 2018 International Electrical Engineering Congress ( iEECON'18), 2018, Mar 7 - 9, Krabi, Thailand. Piscataway, NJ, USA: IEEE, 2018: 1 -4.

[31] LIU W. Research and implementation of localization algorithm for mobile nodes in WSN. Master Thesis. Shenyang, China: Northeastern University, 2010 (in Chinese).

[32] DU J Z. Research on RSSI-based node localization algorithm in wireless sensor networks. Ph D Thesis. Lanzhou, China: Lanzhou University, 2018 (in Chinese).

Weighted semidefinite programming scheme for wireless positioning with lognormal shadowing

Weighted semidefinite programming scheme for wireless positioning with lognormal shadowing

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