基于WLAN的指纹识别技术是在每个指纹定位测量来自不同APs的接收信号强度RSS(received signal strength,RSS)以构建指纹.然而,指纹的收集由于环境的变化,需要定期更新指纹库以提高准确性.因此,为减少指纹识别的工作量,提出将深度置信网络算法应用到未标记的RSS测量中,利用生成概率模型来表示不同隐含层的层次隐含特征,提取指纹的隐藏特征,通过预训练和调优阶段,从而在尽可能保持定位精度的前提下减少标记指纹.实验结果表明,本算法在仅使用15%标记指纹时,将定位精度提高了1.876 m.
WLAN-based fingerprint recognition technology measures Received Signal Strength(RSS)from different APs at a given location of each fingerprint to construct the fingerprint. However, due to changes in the environment, fingerprint collection needs to be updated regularly to improve accuracy. Therefore, in order to reduce the workload of fingerprint identification, the deep belief network algorithm is applied to the unlabelled RSS measurements, to generate probability model to represent the level of the different hidden layer implied characteristics, to extract the hidden features of fingerprint through preliminary training and tuning phase, and to keep the positioning accuracy under the premise of reducing tag fingerprints as much as possible. Experimental results show that this algorithm improves the positioning accuracy by 1.876m when only 15% of the fingerprint is used.
[1] LIU W W, LIU Y C, BUCKNALL R. A robust localization method for unmanned surface vehicle(USV)navigation using fuzzy adaptive Kalman filtering[J]. IEEE Access, 2019, 7: 46071-46083. DOI:10.1109/access.2019.2909151.
[2] WU C S, XU J G, YANG Z, et al. Gain without pain[J]. Proc ACM Interact Mob Wearable Ubiquitous Technol, 2017, 1(2): 1-19. DOI:10.1145/3090094.
[3] WU H, MO Z L, TAN J J, et al. Efficient indoor localization based on geomagnetism[J]. ACM Trans Sen Netw, 2019, 15(4): 1-25. DOI:10.1145/3342517.
[4] 石柯, 宋小妹, 王信达, 等.多传感器辅助的WiFi信号指纹室内定位技术[J].软件学报, 2019, 30(11):3457-3468.DOI:10.13328/j.cnki.jos.005577.
[5] RIZK H. SoloCell: efficient indoor localization based on limited cell network information and minimal fingerprinting[Z]. The 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, Chicago IL USA. 2019. DOI:10.1145/3347146.3363460.
[6] HINTON G E, OSINDERO S, TEH Y W. A fast learning algorithm for deep belief nets[J]. Neural Comput, 2006, 18(7): 1527-1554. DOI:10.1162/neco.2006.18.7.1527.
[7] TORRES-SOSPEDRA J, JIMÉNEZ A, KNAUTH S, et al. The smartphone-based offline indoor location competition at IPIN 2016: analysis and future work[J]. Sensors, 2017, 17(3): 557. DOI:10.3390/s17030557.
[8] HU C H, PEI H, SI X S, et al. A prognostic model based on DBN and diffusion process for degrading bearing[J]. IEEE Trans Ind Electron, 2020, 67(10): 8767-8777. DOI:10.1109/tie.2019.2947839.
[9] CAI B P, HUANG L, XIE M. Bayesian networks in fault diagnosis[J]. IEEE Trans Ind Inf, 2017, 13(5): 2227-2240. DOI:10.1109/tii.2017.2695583.
[10] 袁非牛,章琳,史劲亭,等.自编码神经网络理论及应用综述[J].计算机学报,2019,42(1):203-230.DOI:10.11897/SP.J.1016.2019.00203.
[11] 刘方园,王水花,张煜东.深度置信网络模型及应用研究综述[J].计算机工程与应用,2018,54(1): 11-18,47.DOI:10.3778/j.issn.1002-8331.1711-0028.
[12] KUSHKI A, PLATANIOTIS K N, VENETSANOPOULOS A N. Kernel-based positioning in wireless local area networks[J]. IEEE Trans Mobile Comput, 2007, 6(6): 689-705. DOI:10.1109/tmc.2007.1017.
[13] RADFORD A, METZ L, CHINTALA S. Unsupervised representation learning with deep convolutional generative adversarial networks[Z]. The International Conference on Learning Representations, San Juan, 2016.
[14] CORTEZ E, DA SILVA A S, GONCALVES M A, et al. ONDUX: on-demand unsupervised learning for information extraction[Z]. The 2010 International Conference on Management of Data-SIGMOD10, Indianapolis, Indiana, USA, 2010. DOI:10.1145/1807167.1807254.
[15] KALSHETTI P, CHAUDHURI P. Unsupervised incremental learning for hand shape and pose estimation[Z].Special Interest Group on Computer Graphics and Interactive Techniques Conference, Los Angeles, 2019. DOI:10.1145/3306214.3338553.
[16] 李新春,郭欣欣.基于可靠AP选择和深度置信网络的室内定位算法[J].计算机应用研究,2018,35(8):2469-2473.DOI:10.3969/j.issn.1001-3695.2018.08.058. (
