Mine Location Algorithm Based on Multiple Linear Regression

Abstract:

This paper put forward a mine location algorithm based on multiple linear regression, which using only simple RSSI value to get a higher location accuracy under long narrow and sensitive mine environment. General RSSI measurement method and its drawbacks are discussed in the paper. In order to acquire smaller location error, we filtered some abnormal RSSI data through Gaussian filter method. And we deduced regression equation according to multiple linear regression principle. Combined with training sample, we got their regression parameter. We did relevant location experiment again in the same environment---40m long and narrow bomb shelter which may imitate mine tunnel to a great extent, which shows that the total errors are limited in 3m and 75% errors are less than 2m. What’s more, it can be extended to infinite measuring range with the same set regression coefficient in similar environment.

• Periodical: Applied Mechanics and Materials (Volumes 58-60)
• Pages: 1830-1835
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.58-60.1830
• Online since: June 2011
• Authors: Xiao Ying Wang, Ying Ge Chen
• Keywords: Gaussian Filtering, Mine Location, Multiple Linear Regression, Wireless Sensor Network (WSN)

References

[1] Nicolescu D, Nath B. Ad-Hoc positioning systems (APS) [C]. Proc. of the 2001 IEEE Global Telecommunications Conf. San Antonio: IEEE Communications Society, 2001. 2926-2931.

[2] Niculescu D, Nath B. DV Based Positioning in Ad Hoc Networks [J]. Journal of Telecommunication Systems, 2003, 22(1/4): 267-280.

[3] He T, Huang CD, Blum BM, Stankovic JA, Abdelzaher T. Range-Free Localization Schemes in Large Scale Sensor Networks [C]. Proc. of the 9th Annual Int'l Conf. on Mobile Computing and Networking. San Diego: ACM Press, 2003. 81-95.

DOI: 10.1145/938985.938995

[4] Rabacy JJ, Ammer MJ, da Silva Jr. JL, Patel D, Roundy S. Picoradio Supports Ad Hoc Ultra-low Power Wireless Networking [J]. Computer, 2000, 33(7): 42-48.

DOI: 10.1109/2.869369

[5] WANG Fu-Bao, SHI Long, REN Fengyuan. Self-Localization Systems and Algorithms for Wireless Sensor Networks [J]. Journal of Software, 2005, 16(5): 857-868.

[6] Girod L, Bychovskiy V, Elson J, Estrin D. Locating tiny sensors in time and space: A case study[C]. Proc. of the 2002 IEEE Int'l Conf. on Computer Design: VLSI in Computers and Processors. Freiburg: IEEE Computer Society, 2002. 214-219.

DOI: 10.1109/iccd.2002.1106773

[7] FANG Zhen, ZHAO Zhan, GUO Peng, ZHANG Yuguo. Analysis of Distance Measurement Based on RSSI [J]. Chinese Journal of Sensors and Actuators, 2007, 20(11): 2526-2530.

[8] Sheng Ju, Xie Shiqian, Pan Chengyi. Probability and Statistics. [M], Beijing: Higher Education Press, (2008).

[9] YANG Dongyong, GU Dongyuan，FU XiaoJie. An Indoor Location Algorithm Base on RSSI-Similarity Degree [J] Chinese Journal of Sensors and Actuators, 2009, 22(2): 264-268.

[10] Bahl P, Padmanabhan VN. RADAR: An in-building RF-based User Location and Tracking System[C]. Proc. of the IEEE INFOCOM 2000. Tel Aviv: IEEE Computer and Communications Societies, 2000. 775-784.

DOI: 10.1109/infcom.2000.832252