Indoor positioning algorithm with dynamic environment attenuation based on particle filtering

doi:10.11772/j.issn.1001-9081.2015.09.2465

Journal of Computer Applications ›› 2015, Vol. 35 ›› Issue (9): 2465-2469.DOI: 10.11772/j.issn.1001-9081.2015.09.2465

Previous Articles Next Articles

Indoor positioning algorithm with dynamic environment attenuation based on particle filtering

LI Yinuo, XIAO Ruliang, NI Youcong, SU Xiaomin, DU Xin, CAI Shengzhen

1. Faculty of Software, Fujian Normal University, Fuzhou Fujian 350117, China;
2. Fujian Provincial University Engineering Research Center of Big Data Analysis and Application, Fuzhou Fujian 350117, China

Received:2015-04-20 Revised:2015-05-28 Online:2015-09-10 Published:2015-09-17

基于动态环境衰减的粒子滤波室内定位算法

李奕诺, 肖如良, 倪友聪, 苏小敏, 杜欣, 蔡声镇

1. 福建师范大学软件学院, 福州 350117;
2. 大数据分析与应用福建省高校工程研究中心, 福州 350117

通讯作者: 肖如良(1966-),男,湖南娄底人,教授,博士,CCF会员,主要研究方向:算法设计与分析、软件工程,xiaoruliang@fjnu.edu.cn
作者简介:李奕诺(1989-),男,河南驻马店人,硕士研究生,主要研究方向:机器学习;倪友聪(1975-),男,安徽合肥人,副教授,博士,主要研究方向:软件工程;苏小敏(1994-),女,安徽亳州人,主要研究方向:算法设计;杜欣(1979-),女,新疆石河子人,副教授,博士,主要研究方向:算法设计与分析;蔡声镇(1954-),男,福建泉州人,教授,主要研究方向:嵌入式系统。
基金资助:
教育部规划基金资助项目(11YJA860028);福建省科技计划重大项目(2011H6006)。

Abstract

Abstract: Due to the problem that the nodes having the same distance but different position in the complex environment, brings shortage to accuracy and stability of indoor positioning, a new indoor positioning algorithm with Dynamic Environment Attenuation Factor (DEAF) was proposed. This algorithm built a DEAF model and redefined the way to assume the value. In this algorithm, particle filtering method was firstly used to smooth the Received Signal Strength Indication (RSSI); then, the DEAF model was used to calculate the estimation distance of the node; finally, the trilateration was used to get the position of the target node. Comparative experiments had been done using several filtering models, and the results show that this dynamic environment attenuation factor model combined with particle filtering can resolve the problem of the environment difference very well. This algorithm reduces the mean error to about 0.68 m, and the result has higher positioning accuracy and good stability.

Key words: Dynamic Environment Attenuation Factor (DEAF), particle filtering, Received Signal Strength Indication (RSSI), trilateration, indoor positioning

摘要： 针对精确的室内定位中节点受复杂环境的干扰带来因距离相同而位置不同的环境差异,造成定位精度不足和定位稳定性较差的问题,提出了一种新的动态环境衰减因子(DEAF)模型的算法。算法构造DEAF模型,且重新定义了其取值方式。在算法中,首先利用粒子滤波算法对接收到的信号强度(RSSI)进行平滑处理;然后利用DEAF模型计算目标节点的估计距离;最后用三边测量法求出目标节点的坐标。通过与几种常用的滤波模型进行对比实验,得出这种动态环境衰减模型结合粒子滤波的算法能很好地调和不同位置带来的环境噪声差异,算法使定位平均误差降到0.68 m左右,且在室内定位中有较高的定位精度和较好的稳定性。

关键词: 动态环境衰减因素, 粒子滤波, 接收信号强度, 三边测量法, 室内定位

CLC Number:

TP319

LI Yinuo, XIAO Ruliang, NI Youcong, SU Xiaomin, DU Xin, CAI Shengzhen. Indoor positioning algorithm with dynamic environment attenuation based on particle filtering[J]. Journal of Computer Applications, 2015, 35(9): 2465-2469.

李奕诺, 肖如良, 倪友聪, 苏小敏, 杜欣, 蔡声镇. 基于动态环境衰减的粒子滤波室内定位算法[J]. 计算机应用, 2015, 35(9): 2465-2469.

References

[1] WANG L, XU L, BI Z, et al. Data cleaning for RFID and WSN integration [J]. IEEE Transactions on Industrial Informatics, 2014,10(1):408-418.
[2] ZHOU D. Research on indoor positioning system based on radio frequency identification [D]. Xi'an: Xi'an University of Science and Technology, 2013:9-20.(周惇.基于射频识别的室内定位系统研究[D].西安:西安科技大学,2013:9-20.)
[3] ADNAN T, DATTA S, MacLEAN S. Efficient and accurate sensor network localization [J]. Personal and Ubiquitous Computing, 2014,18(4):821-833.
[4] BAHL P, PADMANABHAN V N. RADAR: an in-building RF-based user location and tracking system [EB/OL]. [2015-01-21]. http://research.microsoft.com/pubs/68671/infocom2000.pdf.
[5] LIN J, XIAO X. An indoor location algorithm based on the wireless network node similarity [J]. Control Engineering of China, 2012,19(1):106-109.(林景栋,萧绪泉.基于无线网络节点相似度的室内定位算法[J].控制工程,2012,19(1):106-109.)
[6] SHI W, XIONG Z, XU L. In-building RSSI-based user localization algorithm [J]. Computer Engineering and Applications, 2010,46(17):232-235.(石为人,熊志广,许磊.一种用于室内人员定位的 RSSI 定位算法[J].计算机工程与应用,2010,46(17):232-235.)
[7] YU C, ZHANG J, DONG Y, et al. Realization on indoor positioning in basis of reference tag [J]. International Journal of Wavelets, Multiresolution and Information Processing, 2014,12(5):494-496.
[8] ZHU Y, DENG Z, LIU W, et al. Multi-classification algorithm for indoor positioning based on support vector machine [J]. Computer Science, 2012,39(4):32-35.(朱宇佳,邓中亮,刘文龙,等.基于支持向量机多分类的室内定位系统[J].计算机科学,2012,39(4):32-35.)
[9] ZHANG Z, DAI L, LI J, et al. Research on optimization weighted centroid localization algorithm based on RSSI [J]. Electronic Design Engineering, 2013,21(7):171-173.(张正华,戴磊,黎家文,等.基于RSSI 的优化加权质心定位算法研究[J].电子设计工程,2013,21(7):171-173.)
[10] XIAO R, LI Y, JIANG S, et al. Indoor positioning based on Kalman filter and weighted median [J]. Journal of Computer Applications, 2014,34(12):3387-3390.(肖如良,李奕诺,江少华,等.基于卡尔曼滤波与中位加权的定位算法[J].计算机应用,2014,34(12):3387-3390.)
[11] BEKKALI A, SANSON H, MATSUMOTO M. RFID indoor positioning based on probabilistic RFID map and Kalman filtering [C]//Proceedings of the 2007 3rd IEEE International Conference on Wireless and Mobile Computing, Networking and Communications. Piscataway: IEEE, 2007:21-21.
[12] STOYANOVA T, KERASIOTIS F, PRAYATI A, et al. A practical RF propagation model for wireless network sensors [C]//Proceedings of the 2009 3rd International Conference on Sensor Technologies and Applications. Piscataway: IEEE, 2009:194-199.
[13] YAN X, QIAN H, SHI W, et al. A weighted location algorithm using statistics median [J]. Transducer and Microsystem Technologies, 2011,31(8):120-123.(严筱永,钱焕延,施卫娟,等.一种利用统计中值的加权定位算法[J].传感器与微系统,2011,31(8):120-123.)
[14] HU S, JING Z. Overview of particle filter algorithm [J]. Control and Decision, 2005,20(4):361-366.(胡士强,敬忠良.粒子滤波算法综述[J].控制与决策,2005,20(4):361-366.)
[15] FENG C, WANG M, JI Q. Analysis and comparison of resampling algorithms in particle filter [J]. Journal of System Simulation, 2009,21(4):1101-1105.(冯驰,王萌,汲清波.粒子滤波器重采样算法的分析与比较[J].系统仿真学报,2009,21(4):1101-1105.)
[16] WANG X, LIU J. Indoor three-dimension location algorithms based on RSSI ranging [J]. Communication and Information Processing, 2011,30(7):63-65.(王晓旭,刘金桂.基于RSSI测距的室内三维定位算法[J].自动化技术与应用,2011,30(7):63-65.)

Indoor positioning algorithm with dynamic environment attenuation based on particle filtering

基于动态环境衰减的粒子滤波室内定位算法

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

[1]	ZHANG Jianming, SHI Yuanhao, XU Zhengyi, WEI Jianming. Adaptive UWB/PDR fusion positioning algorithm based on error prediction [J]. Journal of Computer Applications, 2020, 40(6): 1755-1762.
[2]	ZHANG Bang, ZHU Jinxin, XU Zhengyi, LIU Pan, WEI Jianming. Bayesian network-based floor localization algorithm [J]. Journal of Computer Applications, 2019, 39(8): 2468-2474.
[3]	DING Jianli, MU Tao, WANG Huaichao. Fast indoor positioning algorithm of airport terminal based on spectral regression kernel discriminant analysis [J]. Journal of Computer Applications, 2019, 39(1): 256-261.
[4]	HAO Dehua, GUAN Weiguo, ZOU Linjie, JIAO Meng. Fast virtual grid matching localization algorithm based on Pearson correlation coefficient [J]. Journal of Computer Applications, 2018, 38(3): 763-768.
[5]	LIU Pan, ZHANG Bang, HUANG Chao, YANG Weijun, XU Zhengyi. Pedestrian heading particle filter correction method with indoor environment constraints [J]. Journal of Computer Applications, 2018, 38(12): 3360-3366.
[6]	ZHANG Tao, MA Lei, MEI Lingyu. Indoor positioning method of warehouse mobile robot based on monocular vision [J]. Journal of Computer Applications, 2017, 37(9): 2491-2495.
[7]	BIAN Jiaxing, ZHU Rong, CHEN Xuan. Ultra wideband localization system based on improved two-way ranging and time difference of arrival positioning algorithm [J]. Journal of Computer Applications, 2017, 37(9): 2496-2500.
[8]	LI Lina, LI Wenhao, YOU Hongxiang, WANG Yue. High efficient construction of location fingerprint database based on matrix completion improved by backtracking search optimization [J]. Journal of Computer Applications, 2017, 37(7): 1893-1899.
[9]	WANG Xuqiao, WANG Jinkun. Integrated indoor positioning algorithm based on D-S evidence theory [J]. Journal of Computer Applications, 2017, 37(4): 1198-1201.
[10]	LI Xinchun, HOU Yue. Indoor positioning technology based on improved access point selection and K nearest neighbor algorithm [J]. Journal of Computer Applications, 2017, 37(11): 3276-3280.
[11]	ZHOU Rui, LI Zhiqiang, LUO Lei. WiFi-pedestrian dead reckoning fused indoor positioning based on particle filtering [J]. Journal of Computer Applications, 2016, 36(5): 1188-1191.
[12]	WANG Yufan, AI Haojun, TU Weiping. Wi-Fi fingerprinting clustering for indoor place of interest positioning [J]. Journal of Computer Applications, 2016, 36(2): 488-491.
[13]	CHEN Weiqiang, CHEN Jun, ZHANG Chuang, SONG Liguo, TAN Zhuoli. Multisensor information fusion algorithm based on intelligent particle filtering [J]. Journal of Computer Applications, 2016, 36(12): 3358-3362.
[14]	YUAN Guangjie, LI Xiaodong, JIANG Zhaoyi, YUAN Peng, GUO Zhiwei. Mobile terminal positioning method driven by road test data [J]. Journal of Computer Applications, 2016, 36(12): 3515-3520.
[15]	ZHAO Haijun, CUI Mengtian, LI Mingdong, LI Jia. Node localization based on improved flooding broadcast and particle filtering in wireless sensor network [J]. Journal of Computer Applications, 2016, 36(10): 2659-2663.