CCpos: WiFi Fingerprint Indoor Positioning System Based on CDAE-CNN
WiFi is widely used for indoor positioning because of its advantages such as long transmission distance and ease of use indoors. To improve the accuracy and robustness of indoor WiFi fingerprint localization technology, this paper proposes a positioning system CCPos (CADE-CNN Positioning), which is...
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| Published in: | Sensors (Basel, Switzerland) Vol. 21; no. 4; p. 1114 |
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| Abstract | WiFi is widely used for indoor positioning because of its advantages such as long transmission distance and ease of use indoors. To improve the accuracy and robustness of indoor WiFi fingerprint localization technology, this paper proposes a positioning system CCPos (CADE-CNN Positioning), which is based on a convolutional denoising autoencoder (CDAE) and a convolutional neural network (CNN). In the offline stage, this system applies the K-means algorithm to extract the validation set from the all-training set. In the online stage, the RSSI is first denoised and key features are extracted by the CDAE. Then the location estimation is output by the CNN. In this paper, the Alcala Tutorial 2017 dataset and UJIIndoorLoc are adopted to verify the performance of the CCpos system. The experimental results show that our system has excellent noise immunity and generalization performance. The mean positioning errors on the Alcala Tutorial 2017 dataset and the UJIIndoorLoc are 1.05 m and 12.4 m, respectively. |
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| AbstractList | WiFi is widely used for indoor positioning because of its advantages such as long transmission distance and ease of use indoors. To improve the accuracy and robustness of indoor WiFi fingerprint localization technology, this paper proposes a positioning system CCPos (CADE-CNN Positioning), which is based on a convolutional denoising autoencoder (CDAE) and a convolutional neural network (CNN). In the offline stage, this system applies the K-means algorithm to extract the validation set from the all-training set. In the online stage, the RSSI is first denoised and key features are extracted by the CDAE. Then the location estimation is output by the CNN. In this paper, the Alcala Tutorial 2017 dataset and UJIIndoorLoc are adopted to verify the performance of the CCpos system. The experimental results show that our system has excellent noise immunity and generalization performance. The mean positioning errors on the Alcala Tutorial 2017 dataset and the UJIIndoorLoc are 1.05 m and 12.4 m, respectively. WiFi is widely used for indoor positioning because of its advantages such as long transmission distance and ease of use indoors. To improve the accuracy and robustness of indoor WiFi fingerprint localization technology, this paper proposes a positioning system CCPos (CADE-CNN Positioning), which is based on a convolutional denoising autoencoder (CDAE) and a convolutional neural network (CNN). In the offline stage, this system applies the K-means algorithm to extract the validation set from the all-training set. In the online stage, the RSSI is first denoised and key features are extracted by the CDAE. Then the location estimation is output by the CNN. In this paper, the Alcala Tutorial 2017 dataset and UJIIndoorLoc are adopted to verify the performance of the CCpos system. The experimental results show that our system has excellent noise immunity and generalization performance. The mean positioning errors on the Alcala Tutorial 2017 dataset and the UJIIndoorLoc are 1.05 m and 12.4 m, respectively.WiFi is widely used for indoor positioning because of its advantages such as long transmission distance and ease of use indoors. To improve the accuracy and robustness of indoor WiFi fingerprint localization technology, this paper proposes a positioning system CCPos (CADE-CNN Positioning), which is based on a convolutional denoising autoencoder (CDAE) and a convolutional neural network (CNN). In the offline stage, this system applies the K-means algorithm to extract the validation set from the all-training set. In the online stage, the RSSI is first denoised and key features are extracted by the CDAE. Then the location estimation is output by the CNN. In this paper, the Alcala Tutorial 2017 dataset and UJIIndoorLoc are adopted to verify the performance of the CCpos system. The experimental results show that our system has excellent noise immunity and generalization performance. The mean positioning errors on the Alcala Tutorial 2017 dataset and the UJIIndoorLoc are 1.05 m and 12.4 m, respectively. |
| Author | Wang, Xing Zuo, Tao Qin, Feng |
| AuthorAffiliation | 2 Engineering Research Center for Metallurgical Automation and Detecting Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China; wangxing199613@163.com 1 College of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan 430081, China; QinF7373@163.com |
| AuthorAffiliation_xml | – name: 1 College of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan 430081, China; QinF7373@163.com – name: 2 Engineering Research Center for Metallurgical Automation and Detecting Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China; wangxing199613@163.com |
| Author_xml | – sequence: 1 givenname: Feng surname: Qin fullname: Qin, Feng – sequence: 2 givenname: Tao surname: Zuo fullname: Zuo, Tao – sequence: 3 givenname: Xing surname: Wang fullname: Wang, Xing |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/33562754$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1016/j.eswa.2015.08.013 10.1109/ISCC.2018.8538530 10.1109/JIOT.2020.2986685 10.1109/UPINLBS.2018.8559903 10.3390/s17040812 10.1109/JIOT.2018.2864607 10.1016/j.procir.2017.03.085 10.1109/PERCOM.2019.8767421 10.1109/ICUFN.2018.8436598 10.1109/IPIN.2014.7275492 10.1016/j.pmcj.2019.101085 10.1016/j.jfranklin.2019.10.028 10.1016/j.compeleceng.2020.106694 10.1016/j.promfg.2019.02.021 10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00139 10.1109/JIOT.2019.2940368 10.1016/j.sigpro.2019.107375 10.1016/j.icte.2018.04.004 10.1109/UPINLBS.2018.8559705 10.1109/IPIN.2014.7275556 10.1109/IPIN.2017.8115940 10.1016/j.future.2020.03.043 10.3390/s19092114 10.1016/j.future.2018.06.030 10.1109/ICISCE.2017.354 10.1109/ICDSP.2018.8631593 |
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| References | Zhang (ref_13) 2020; 7 Hu (ref_17) 2019; 6 ref_14 Hoang (ref_19) 2019; 6 Bialer (ref_7) 2020; 169 ref_10 ref_30 Thewan (ref_11) 2019; 30 ref_18 ref_16 Orujov (ref_4) 2018; 89 Gao (ref_8) 2020; 86 Zh (ref_2) 2020; 357 Oh (ref_6) 2018; 4 ref_25 ref_24 ref_23 ref_22 ref_21 Huang (ref_3) 2017; 63 ref_20 ref_1 Ninh (ref_12) 2020; 109 Yang (ref_15) 2019; 60 ref_29 Montoliu (ref_27) 2015; 42 ref_26 Song (ref_28) 2020; 31 ref_9 ref_5 |
| References_xml | – volume: 42 start-page: 9263 year: 2015 ident: ref_27 article-title: Comprehensive analysis of distance and similarity measures for Wi-Fi fingerprinting indoor positioning systems publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2015.08.013 – ident: ref_21 doi: 10.1109/ISCC.2018.8538530 – volume: 7 start-page: 10773 year: 2020 ident: ref_13 article-title: WiFi-Based Indoor Robot Positioning Using Deep Fuzzy Forests publication-title: IEEE Internet Things J. doi: 10.1109/JIOT.2020.2986685 – ident: ref_18 doi: 10.1109/UPINLBS.2018.8559903 – ident: ref_24 – ident: ref_26 – ident: ref_5 doi: 10.3390/s17040812 – volume: 6 start-page: 891 year: 2019 ident: ref_17 article-title: Experimental Analysis on Weight K -Nearest Neighbor Indoor Fingerprint Positioning publication-title: IEEE Internet Things J. doi: 10.1109/JIOT.2018.2864607 – volume: 63 start-page: 132 year: 2017 ident: ref_3 article-title: A Real-time Location System Based on RFID and UWB for Digital Manufacturing Workshop publication-title: Procedia CIRP doi: 10.1016/j.procir.2017.03.085 – ident: ref_16 – ident: ref_20 doi: 10.1109/PERCOM.2019.8767421 – ident: ref_22 doi: 10.1109/ICUFN.2018.8436598 – ident: ref_29 doi: 10.1109/IPIN.2014.7275492 – volume: 60 start-page: 101085 year: 2019 ident: ref_15 article-title: WKNN indoor location algorithm based on zone partition by spatial features and restriction of former location publication-title: Pervasive Mob. Comput. doi: 10.1016/j.pmcj.2019.101085 – volume: 357 start-page: 1420 year: 2020 ident: ref_2 article-title: Accurate WiFi-based indoor localization by using fuzzy classifier and mlps ensemble in complex environment publication-title: J. Franklin Inst. doi: 10.1016/j.jfranklin.2019.10.028 – volume: 86 start-page: 106694 year: 2020 ident: ref_8 article-title: An improved localization method in cyber-social environments with obstacles publication-title: Comput. Electr. Eng. doi: 10.1016/j.compeleceng.2020.106694 – volume: 30 start-page: 143 year: 2019 ident: ref_11 article-title: Comparing WiFi RSS Filtering for Wireless Robot Location System publication-title: Procedia Manuf. doi: 10.1016/j.promfg.2019.02.021 – ident: ref_23 doi: 10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00139 – volume: 6 start-page: 10639 year: 2019 ident: ref_19 article-title: Recurrent Neural Networks for Accurate RSSI Indoor Localization publication-title: IEEE Internet Things J. doi: 10.1109/JIOT.2019.2940368 – volume: 169 start-page: 107375 year: 2020 ident: ref_7 article-title: A time-of-arrival estimation algorithm for OFDM signals in indoor multipath environments publication-title: Signal Process. doi: 10.1016/j.sigpro.2019.107375 – volume: 4 start-page: 91 year: 2018 ident: ref_6 article-title: Adaptive K-nearest neighbour algorithm for WiFi fingerprint positioning publication-title: ICT Express doi: 10.1016/j.icte.2018.04.004 – ident: ref_9 doi: 10.1109/UPINLBS.2018.8559705 – ident: ref_14 doi: 10.1109/IPIN.2014.7275556 – ident: ref_30 doi: 10.1109/IPIN.2017.8115940 – volume: 109 start-page: 238 year: 2020 ident: ref_12 article-title: An effective random statistical method for Indoor Positioning System using WiFi fingerprinting publication-title: Future Gener. Comput. Syst. doi: 10.1016/j.future.2020.03.043 – ident: ref_1 doi: 10.3390/s19092114 – volume: 31 start-page: 25 year: 2020 ident: ref_28 article-title: Image restoration using convolutional denoising autoencoder in images publication-title: J. Korean Data Inf. Sci. Soc. – volume: 89 start-page: 335 year: 2018 ident: ref_4 article-title: Smartphone based intelligent indoor positioning using fuzzy logic publication-title: Future Gener. Comput. Syst. doi: 10.1016/j.future.2018.06.030 – ident: ref_10 doi: 10.1109/ICISCE.2017.354 – ident: ref_25 doi: 10.1109/ICDSP.2018.8631593 |
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| Title | CCpos: WiFi Fingerprint Indoor Positioning System Based on CDAE-CNN |
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