Hybrid Fingerprint Indoor Localization Method Based on ECA-CNN

Wang Zhengkang; Luo Bingqing

doi:10.7544/issn1000-1239.202220568

Journal of Computer Research and Development > 2024 > 61(2): 428-440. > DOI: 10.7544/issn1000-1239.202220568

Wang Zhengkang, Luo Bingqing. Hybrid Fingerprint Indoor Localization Method Based on ECA-CNN[J]. Journal of Computer Research and Development, 2024, 61(2): 428-440. DOI: 10.7544/issn1000-1239.202220568

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Hybrid Fingerprint Indoor Localization Method Based on ECA-CNN

Wang Zhengkang,
Luo Bingqing^,

School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023

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Author Bio:
Wang Zhengkang: born in 1997. Master candidate. His main research interests include indoor positioning technology and computer application technology

Luo Bingqing: born in 1987. PhD, associate professor, master supervisor. Her main research interests include wireless network communications, blockchain, and Internet of things
Received Date: June 15, 2022
Revised Date: March 08, 2023
Available Online: November 09, 2023

Graphical Abstract

Abstract

Abstract

Fingerprint features and localization model are two key factors affecting the localization accuracy in fingerprint localization methods. In terms of fingerprint feature, the stability of visible light intensity is high, but the discrimination of position features is low. The radio signal strength is highly distinguishable, but has strong volatility. Meanwhile, the localization models based on convolutional neural network (CNN) can not effectively highlight important features during feature extraction. In view of the above problems, a hybrid fingerprint indoor localization method based on ECA-CNN (ECACon-HF) is proposed in this paper. First, we use visible light intensity and received signal strength indication (RSSI) of Bluetooth low energy (BLE) to construct hybrid fingerprints, reduce the influence of instability of BLE fingerprints, and enhance the discrimination between different positions. Meanwhile, the CNN localization model is improved by the efficient channel attention (ECA). ECA can adaptively extract important information from fingerprints through cross-channel interaction strategies, suppress environmental interference in fingerprints, enhance the expression ability of hybrid fingerprint features, and make more effective use of the advantages of hybrid fingerprints. The experimental results show that ECACon-HF proposed in this paper achieves the localization precision of 0.316 m, higher accuracy than on the single fingerprint. Meanwhile, based on the same fingerprint database, the proposed method outperforms other related indoor localization methods.
- indoor localization,
- hybrid fingerprint,
- visible light intensity,
- attention mechanism,
- Bluetooth low energy (BLE)

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References (30)

References

[1]	Rusli M E, Ali M, Jamil N, et al. An improved indoor positioning algorithm based on RSSI-trilateration technique for Internet of things (IoT) [C]//Proc of 2016 Int Conf on Computer and Communication Engineering (ICCCE). Piscataway, NJ: IEEE, 2016: 72−77
[2]	Giuliano R, Cardarilli G C, Cesarini C, et al. Indoor localization system based on Bluetooth low energy for museum applications[J]. Electronics, 2020, 9(6): 1055 doi: 10.3390/electronics9061055
[3]	张勇,李飞腾,王昱洁. 基于KDDA和SFLA-LSSVR算法的WLAN室内定位算法[J]. 计算机研究与发展,2017,54(5):979−985 doi: 10.7544/issn1000-1239.2017.20160025 Zhang Yong, Li Feiteng, Wang Yujie. Indoor positioning algorithm for WLAN based on KDDA and SFLA-LSSVR[J]. Journal of Computer Research and Development, 2017, 54(5): 979−985 (in Chinese) doi: 10.7544/issn1000-1239.2017.20160025
[4]	Bianchi V, Ciampolini P, De Munari I. RSSI-based indoor localization and identification for ZigBee wireless sensor networks in smart homes[J]. IEEE Transactions on Instrumentation and Measurement, 2018, 68(2): 566−575
[5]	Zhu Xiaoqiang, Qu Wenyu, Qiu Tie, et al. Indoor intelligent fingerprint-based localization: Principles, approaches and challenges[J]. IEEE Communications Surveys & Tutorials, 2020, 22(4): 2634−2657
[6]	杨国伟,黄兆标,樊冰,等. 基于可见光通信的室内定位与定向系统[J]. 通信学报,2020,41(12):162−170 Yang Guowei, Huang Zhaobiao, Fan Bing, et al. Indoor positioning and orientating system based on visible light communication[J]. Journal on Communications, 2020, 41(12): 162−170 (in Chinese)
[7]	Liu Ning, He Tao, He Suining, et al. Indoor localization with adaptive signal sequence representations[J]. IEEE Transactions on Vehicular Technology, 2021, 70(11): 11678−11694 doi: 10.1109/TVT.2021.3113333
[8]	Xiang Peng, Ji Peng, Zhang Dian. Enhance RSS-based indoor localization accuracy by leveraging environmental physical features[J]. Wireless Communications and Mobile Computing, 2018, 2018: 8956757
[9]	Zhou Chengyi, Liu Junyu, Sheng Min, et al. Exploiting fingerprint correlation for fingerprint-based indoor localization: A deep learning based approach[J]. IEEE Transactions on Vehicular Technology, 2021, 70(6): 5762−5774 doi: 10.1109/TVT.2021.3075539
[10]	Wang Xuyu, Yu Zhitao, Mao Shiwen. Indoor localization using smartphone magnetic and light sensors: A deep LSTM approach[J]. Mobile Networks and Applications, 2020, 25(2): 819−832 doi: 10.1007/s11036-019-01302-x
[11]	Kanaris L, Kokkinis A, Liotta A, et al. Fusing Bluetooth beacon data with Wi-Fi radiomaps for improved indoor localization[J]. Sensors, 2017, 17(4): 812 doi: 10.3390/s17040812
[12]	吴虹,王国萍,彭鸿钊,等. 一种基于KNN的室内位置指纹定位算法[J]. 南开大学学报:自然科学版,2020,53(6):5−9 Wu Hong, Wang Guoping, Peng Hongzhao, et al. A location fingerprinting algorithm for indoor positioning based on K-nearest neighbors[J]. Acta Scientiarum Naturalium Universitatis Nankaiensis, 2020, 53(6): 5−9 (in Chinese)
[13]	Xie Yaqin, Wang Yan, Nallanathan A, et al. An improved K-nearest neighbor indoor localization method based on spearman distance[J]. IEEE Signal Processing Letters, 2016, 23(3): 351−355 doi: 10.1109/LSP.2016.2519607
[14]	Zhang Guowei, Xu Zhan, Liu Dan. Research and improvement on indoor localization based on RSSI fingerprint database and K-nearest neighbor points[C]//Proc of 2013 Int Conf on Communications, Circuits and Systems (ICCCAS). Piscataway, NJ: IEEE, 2013: 68−71
[15]	Peng Xuesheng, Chen Ruizhi, Yu Kegen, et al. An improved weighted K-nearest neighbor algorithm for indoor localization[J]. Electronics, 2020, 9(12): 2117 doi: 10.3390/electronics9122117
[16]	田洪亮,钱志鸿,梁潇,等. 离散度WKNN位置指纹Wi-Fi定位算法[J]. 哈尔滨工业大学学报,2017,49(5):94−99 doi: 10.11918/j.issn.0367-6234.201610104 Tian Hongliang, Qian Zhihong, Liang Xiao, et al. Discrete degree WKNN location fingerprinting algorithm based on Wi-Fi[J]. Journal of Harbin Institute of Technology, 2017, 49(5): 94−99 (in Chinese) doi: 10.11918/j.issn.0367-6234.201610104
[17]	Xue Weixing, Hua Xianghong, Li Qingquan, et al. A new weighted algorithm based on the uneven spatial resolution of RSSI for indoor localization[J]. IEEE Access, 2018, 6: 26588−26595 doi: 10.1109/ACCESS.2018.2837018
[18]	Soro B, Lee C. Joint time-frequency RSSI features for convolutional neural network-based indoor fingerprinting localization[J]. IEEE Access, 2019, 7: 104892−104899 doi: 10.1109/ACCESS.2019.2932469
[19]	Chen Hao, Zhang Yifan, Li Wei, et al. ConFi: Convolutional neural networks based indoor Wi-Fi localization using channel state information[J]. IEEE Access, 2017, 5: 18066−18074 doi: 10.1109/ACCESS.2017.2749516
[20]	Wang Xuyu, Wang Xiangyu, Mao Shiwen. Deep convolutional neural networks for indoor localization with CSI images[J]. IEEE Transactions on Network Science and Engineering, 2018, 7(1): 316−327
[21]	Shu Yuanchao, Bo Cheng, Shen Guobin, et al. Magicol: Indoor localization using pervasive magnetic field and opportunistic Wi-Fi sensing[J]. IEEE Journal on Selected Areas in Communications, 2015, 33(7): 1443−1457 doi: 10.1109/JSAC.2015.2430274
[22]	Wang Yijin, Xu Xiaolong. Indoor localization service based on hybrid fingerprint map[C]//Proc of the 24th IEEE Int Conf on Web Services (ICWS). Piscataway, NJ: IEEE, 2017: 452−459
[23]	Jang J W, Hong S N. Indoor localization with Wi-Fi fingerprinting using convolutional neural network[C]//Proc of the 10th Int Conf on Ubiquitous and Future Networks (ICUFN). Piscataway, NJ: IEEE, 2018: 753−758
[24]	Ashraf I, Kang M, Hur S, et al. MINLOC: Magnetic field patterns-based indoor localization using convolutional neural networks[J]. IEEE Access, 2020, 8: 66213−66227 doi: 10.1109/ACCESS.2020.2985384
[25]	Zhao Bobai, Zhu Dali, Xi Tong, et al. Convolutional neural network and dual-factor enhanced variational Bayes adaptive Kalman filter based indoor localization with Wi-Fi[J]. Computer Networks, 2019, 162: 106864 doi: 10.1016/j.comnet.2019.106864
[26]	Liu Zhenyu, Dai Bin, Wan Xiang, et al. Hybrid wireless fingerprint indoor localization method based on a convolutional neural network[J]. Sensors, 2019, 19(20): 4597 doi: 10.3390/s19204597
[27]	Du Wenju, Rao Nini, Dong Changlong, et al. Automatic classification of esophageal disease in gastroscopic images using an efficient channel attention deep dense convolutional neural network[J]. Biomedical Optics Express, 2021, 12(6): 3066−3081 doi: 10.1364/BOE.420935
[28]	Shu Xin, Chang Feng, Zhang Xin, et al. ECAU-Net: Efficient channel attention U-Net for fetal ultrasound cerebellum segmentation[J]. Biomedical Signal Processing and Control, 2022, 75: 103528 doi: 10.1016/j.bspc.2022.103528
[29]	Xiang Xuezhi, Ren Wenkai, Qiu Yujian, et al. Multi-object tracking method based on efficient channel attention and switchable atrous convolution[J]. Neural Processing Letters, 2021, 53(4): 2747−2763 doi: 10.1007/s11063-021-10519-5
[30]	Wang Qilong, Wu Banggu, Zhu Pengfei, et al, ECA-net: Efficient channel attention for deep convolutional neural networks[C]//Proc of the 2020 IEEE/CVF Conf on Computer Vision and Pattern Recognition (CVPR). Piscataway, NJ: IEEE, 2020: 11531−11539