Abstract
We propose a novel indoor positioning algorithm based on the received signal strength (RSS) fingerprint. The proposed algorithm can be divided into three steps, an offline phase at which an advanced clustering (AC) strategy is used, an online phase of approximate localization at which cluster matching is used, and an online phase of precise localization with kernel ridge regression. Specifically, after offline fingerprint collection and similarity measurement, we employ an AC strategy based on the K-medoids clustering algorithm using additional reference points that are geographically located at the outer cluster boundary to enrich the data of each cluster. During the approximate localization, RSS measurements are compared with the cluster radio maps to determine to which cluster the target most likely belongs. Both the Euclidean distance of the RSSs and the Hamming distance of the coverage vectors between the observations and training records are explored for cluster matching. Then, a kernel-based ridge regression method is used to obtain the ultimate positioning of the target. The performance of the proposed algorithm is evaluated in two typical indoor environments, and compared with those of state-of-the-art algorithms. The experimental results demonstrate the effectiveness and advantages of the proposed algorithm in terms of positioning accuracy and complexity.
摘要
智能移动设备和无线传感器网络相关技术的融合发展, 使得基于位置的服务受到广泛关注. 如何利用无线信号在室内复杂环境下实时获得理想的定位精度, 成为当前研究热点之一. 提出一种基于接收信号强度 (RSS, received signal strength) 的位置指纹定位算法. 该算法分为离线和在线阶段. 离线阶段采用一种改进的分簇方法, 采用K中心点分簇算法, 把物理位置位于簇外边缘的参考点加入簇指纹库, 使得参考位置点的RSS信号特性与物理位置结合. 在线定位时, 基于簇匹配的粗定位与簇内二次精确定位结合. 簇内定位采用基于核岭回归的算法, 通过核函数实现RSS信号特性与物理位置非线性关系挖掘, 同时算法在簇内成员中进行, 减小了时间复杂度. 通过两个典型室内环境下的定位实验, 探究了基于RSS信号强度和覆盖向量的两种分簇和簇匹配准则对算法性能的影响, 以及不同环境下参数的选择, 验证了所提算法的定位性能.
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References
Al-Moukhles H, Jaber AK, Abdel-Qader I, 2016. Impact of APs selection scheme on compressive sensing- fingerprinting based IPS performance. Proc IEEE 7th Annual Ubiquitous Computing, Electronics & Mobile Communication Conf, p.1–7. https://doi.org/10.1109/uemcon.2016.7777849
Al Nuaimi K, Kamel H, 2011. A survey of indoor positioning systems and algorithms. Proc Int Conf on Innovations in Information Technology, p.185–190. https://doi.org/10.1109/innovations.2011.5893813
Bahl P, Padmanabhan VN, 2000. RADAR: an in-building RF-based user location and tracking system. Proc IEEE Conf on Computer Communications and 19th Annual Joint Conf of the IEEE Computer and Communications Societies, p.775–784. https://doi.org/10.1109/infcom.2000.832252
Chen C, Wang YJ, Zhang Y, et al., 2018. Indoor positioning algorithm based on nonlinear PLS integrated with RVM. IEEE Sens J, 18(2):660–668. https://doi.org/10.1109/jsen.2017.2772798
Chen YQ, Yang Q, Yin J, et al., 2006. Power-efficient accesspoint selection for indoor location estimation. IEEE Trans Knowl Data Eng, 18(7):877–888. https://doi.org/10.1109/TKDE.2006.112
Dai H, Ying WH, Xu J, 2016. Multi-layer neural network for received signal strength-based indoor localisation. IET Commun, 10(6):717–723. https://doi.org/10.1049/iet-com.2015.0469
Fang SH, Lin T, 2012. Principal component localization in indoor WLAN environments. IEEE Trans Mob Comput, 11(1):100–110. https://doi.org/10.1109/tmc.2011.30
Fang XM, Jiang ZH, Nan L, et al., 2018. Optimal weighted K-nearest neighbour algorithm for wireless sensor network fingerprint localisation in noisy environment. IET Commun, 12(10):1171–1177. https://doi.org/10.1049/iet-com.2017.0515
Feng C, Au WSA, Valaee S, et al., 2012. Received-signal-strength-based indoor positioning using compressive sensing. IEEE Trans Mob Comput, 11(12):1983–1993. https://doi.org/10.1109/tmc.2011.216
Harroud H, Ahmed M, Karmouch A, 2003. Policy-driven personalized multimedia services for mobile users. IEEE Trans Mob Comput, 2(1):16–24. https://doi.org/10.1109/tmc.2003.1195148
Honeine P, Mourad F, Kallas M, et al., 2011. Wireless sensor networks in biomedical: body area networks. Proc Int Workshop on Systems, Signal Processing and Their Applications, p.388–391. https://doi.org/10.1109/WOSSPA.2011.5931518
Hu JS, Liu HL, Liu DW, et al., 2018. Reducing Wi-Fi fingerprint collection based on affinity propagation clustering and WKNN interpolation algorithm. Proc 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conf, p.2463–2468. https://doi.org/10.1109/IMCEC.2018.8469697
Huang CC, Manh HN, 2016. RSS-based indoor positioning based on multi-dimensional kernel modeling and weighted average tracking. IEEE Sens J, 16(9):3231–3245. https://doi.org/10.1109/JSEN.2016.2524537
Khalajmehrabadi A, Gatsis N, Pack DJ, et al., 2017a. A joint indoor WLAN localization and outlier detection scheme using LASSO and elastic-net optimization techniques. IEEE Trans Mob Comput, 16(8):2079–2092. https://doi.org/10.1109/tmc.2016.2616465
Khalajmehrabadi A, Gatsis N, Akopian D, 2017b. Structured group sparsity: a novel indoor WLAN localization, outlier detection, and radio map interpolation scheme. IEEE Trans Veh Technol, 66(7):6498–6510. https://doi.org/10.1109/TVT.2016.2631980
Kumar C, Rajawat K, 2019. Dictionary-based statistical fingerprinting for indoor localization. IEEE Trans Veh Technol, 68(9):8827–8841. https://doi.org/10.1109/tvt.2019.2929360
Kushki A, Plataniotis KN, Venetsanopoulos AN, et al., 2007. Kernel-based positioning in wireless local area networks. IEEE Trans Mob Comput, 6(6):689–705. https://doi.org/10.1109/TMC.2007.1017
Li LQ, He Z, Nielsen J, et al., 2015. Using Wi-Fi/magnetometers for indoor location and personal navigation. Proc Int Conf on Indoor Positioning and Indoor Navigation, p.1–7. https://doi.org/10.1109/ipin.2015.7346764
Lu XX, Zou H, Zhou HM, et al., 2016. Robust extreme learning machine with its application to indoor positioning. IEEE Trans Cybern, 46(1):194–205. https://doi.org/10.1109/tcyb.2015.2399420
Maalouf M, Homouz D, 2014. Kernel ridge regression using truncated Newton method. Knowl-Based Syst, 71:339–344. https://doi.org/10.1016/j.knosys.2014.08.012
Mahfouz S, Mourad-Chehade F, Honeine P, et al., 2013. Kernel-based localization using fingerprinting in wireless sensor networks. Proc IEEE 14th Workshop on Signal Processing Advances in Wireless Communications, p.744–748. https://doi.org/10.1109/SPAWC.2013.6612149
Mahfouz S, Mourad-Chehade F, Honeine P, et al., 2016. Non-parametric and semi-parametric RSSI/distance modeling for target tracking in wireless sensor networks. IEEE Sens J, 16(7):2115–2126. https://doi.org/10.1109/JSEN.2015.2510020
Niu JW, Wang BW, Shu L, et al., 2015. ZIL: an energy-efficient indoor localization system using ZigBee radio to detect WiFi fingerprints. IEEE J Sel Areas Commun, 33(7):1431–1442. https://doi.org/10.1109/jsac.2015.2430171
Rodriguez MD, Favela J, Martinez EA, et al., 2004. Location- aware access to hospital information and services. IEEE Trans Inform Technol Biomed, 8(4):448–455. https://doi.org/10.1109/titb.2004.837887
Saunders C, Gammerman A, Vovk V, 1998. Ridge regression learning algorithm in dual variables. Proc 15th Int Conf on Machine Learning, p.515–521.
Shi LF, Wang Y, Liu GX, et al., 2018. A fusion algorithm of indoor positioning based on PDR and RSS fingerprint. IEEE Sens J, 18(23):9691–9698. https://doi.org/10.1109/jsen.2018.2873052
Wang XY, Gao LJ, Mao SW, et al., 2017. CSI-based fingerprinting for indoor localization: a deep learning approach. IEEE Trans Veh Technol, 66(1):763–776. https://doi.org/10.1109/TVT.2016.2545523
Wu Z, Fu KC, Jedari E, et al., 2016. A fast and resource efficient method for indoor positioning using received signal strength. IEEE Trans Veh Technol, 65(12):9749–9758. https://doi.org/10.1109/tvt.2016.2530761
Xue WX, Yu KG, Hua XH, et al., 2018. APs’ virtual positions-based reference point clustering and physical distance-based weighting for indoor Wi-Fi positioning. IEEE Intern Things J, 5(4):3031–3042. https://doi.org/10.1109/jiot.2018.2829486
Yan J, Zhao L, Tang J, et al., 2018. Hybrid kernel based machine learning using received signal strength measurements for indoor localization. IEEE Trans Veh Technol, 67(3):2824–2829. https://doi.org/10.1109/TVT.2017.2774103
Youssef MA, Agrawala A, Shankar AU, 2003. WLAN location determination via clustering and probability distributions. Proc 1st IEEE Int Conf on Pervasive Computing and Communications, p.143–150. https://doi.org/10.1109/PERCOM.2003.1192736
Zhang Y, Li DP, Wang YJ, 2019. An indoor passive positioning method using CSI fingerprint based on Adaboost. IEEE Sens J, 19(14):5792–5800.
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Yanfen LE and Heng YAO designed the research. Yanfen LE and Hena ZHANG processed the data. Yanfen LE drafted the manuscript. Weibin SHI helped organize the manuscript. Yanfen LE and Heng YAO revised and finalized the paper.
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Yanfen LE, Hena ZHANG, Weibin SHI, and Heng YAO declare that they have no conflict of interest.
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Project supported by the National Natural Science Foundation of China (Nos. 51705324 and 61702332)
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Le, Y., Zhang, H., Shi, W. et al. Received signal strength based indoor positioning algorithm using advanced clustering and kernel ridge regression. Front Inform Technol Electron Eng 22, 827–838 (2021). https://doi.org/10.1631/FITEE.2000093
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DOI: https://doi.org/10.1631/FITEE.2000093
Key words
- Indoor positioning
- Received signal strength (RSS) fingerprint
- Kernel ridge regression
- Cluster matching
- Advanced clustering