481
Views
2
CrossRef citations to date
0
Altmetric
eview Article

RSSI-based location fingerprint method for RFID indoor positioning: a review

, , &
Pages 3-31 | Received 01 May 2023, Accepted 23 Aug 2023, Published online: 05 Sep 2023

References

  • Yang L. Research on RFID tag location algorithm based on RSSI. Southeast University. 2019. doi: 10.27014/d.cnki.gdnau.2019.000207
  • Gu Y. Research on indoor positioning solution based on WIFI and RFID. Suzhou University. 2018. doi: 10.27351/d.cnki.gszhu.2018.000298
  • Han ML, Liu Y. Comparison of RFID positioning technology and common algorithms. Ind Technol Forum. 2014;12(1):89–91.
  • Ma Y, Pahlavan K, Geng Y. Comparative behavioral modeling of POA and TOA ranging for location-awareness using RFID. Int J Wireless Inf Networks. 2016;23(3):187–198. doi: 10.1007/s10776-016-0311-6
  • Guzmán-Quirós R, Martínez-Sala A, Gómez-Tornero JL, et al. Integration of directional antennas in an RSS fingerprinting-based indoor localization system. Sens. 2015;16(1):4. doi: 10.3390/s16010004
  • Yassin A, Nasser Y, Awad M, et al. Recent advances in indoor localization: A survey on theoretical approaches and applications. IEEE Commun Surv Tutorials. 2016;19(2):1327–1346. doi: 10.1109/comst.2016.2632427
  • Tao S. Research on filtering and positioning algorithm of WSN node ranging data based on phase difference ranging. Electron Des Eng. 2017;7:35–40.
  • Yang P, Wu W. Efficient particle filter localization algorithm in dense passive RFID tag environment. IEEE Trans Ind Electron. 2014;61(10):5641–5651. doi: 10.1109/tie.2014.2301737
  • Ma N. Research on RFID indoor wireless location algorithm based on RSSI [MA thesis]. University of Electronic Science and Technology of China; 2012. https://kns.cnki.net/KCMS/detail/detail.aspx?dbname=CMFD201301&filename=1012470985.nh.
  • Zanca G, Zorzi F, Zanella A, et al., editors. Experimental comparison of RSSI-based localization algorithms for indoor wireless sensor networks. Proceedings of the workshop on Real-world wireless sensor networks; 2008.doi:10.1145/1435473.1435475.
  • Leng YF, Zhu HP, Alsharari T, et al. An improved RSSI positioning algorithm based on reference distances. Adv Mater Res. 2014; Trans Tech Publ. doi:10.4028/www.scientific.net/amr.971-973.1547
  • Zou X. Research on the algorithm of RSSI location and fingerprint location. Comput Knowl Technol. 2019;28. doi: 10.14004/j.cnki.ckt.2019.3621
  • Qiao GZ, Zeng J. An improved RSSI localisation method for dynamic environments. Comput Res Dev. 2010;S2:111–114.1.
  • Li Y, He X, Liu S. A wireless positioning method based on real-time estimation of path loss model parameters. J Sens Technol. 2010;23(9):1328–1333.
  • Goldoni E, Savioli A, Risi M, et al., editors. Experimental analysis of RSSI-based indoor localization with IEEE 802.15. 4. 2010 European Wireless Conference (EW); 2010: IEEE. doi:10.4103/0377-2063.123755
  • Zhang Z. Some processing methods of RSSI filtering in wireless sensor networks. Mod Electron Technol. 2013;36(20):4–6. doi: 10.16652/j.issn.1004-373x.2013.20.020
  • Henry W. Research and implementation of indoor location algorithm for wireless sensor networks based on RSSI. Beijing: Beijing University of Posts and Telecommunications; 2010.
  • Xiao JN. Hybrid filtering algorithm based on RSSI. Comput Sci. 2019;46(8):133–137.
  • Zhu M, Zhang HQ. Research on indoor ranging model based on RSSI. Sens Microsys. 2010;8:19–22.
  • Wan Q, Guo X. Method and application of indoor positioning. Beijing: Electronic Industry Press; 2012.
  • Dong YY. Research on three-dimensional spatial positioning technology under WiFi network. Beijing: Beijing University of Posts and Telecommunications; 2012.
  • James Z, Zhao H, G SP, et al. Location algorithm of equilateral triangle based on RSSI mean. J Northeast Univ Nat Sci Ed. 1094–1097. CNKI:SUN:DBDX.0.2007-08-008
  • Kalman RE. A new approach to linear filtering and prediction problems. Trans ASME J Basic Eng. 1960;82(1):35–45. doi: 10.1115/1.3662552
  • Zhang J. Signal filtering analysis and simulation. Electron Des Eng. 2017;25(2):45–48. doi: 10.14022/j.cnki.dzsjgc.2017.02.011
  • Caceres MA, Sottile F, Spirito MA Adaptive location tracking by Kalman filter in wireless sensor networks[C]//2009 IEEE international conference on wireless and mobile computing, networking and communications. IEEE, 2009: 123–128. doi:10.1109/WiMob.2009.30.
  • Ai H, Li Y. Weighted centroid localization algorithm based on RSSI ranging filter optimization. Comput Eng Des. 2017;38(10):2631–2635.
  • Shan HM, Chen CX. Artificial Bee Colony localization algorithm based on RSSI Gaussian filter. J Sens Technol. 2021;34(7):979–983. CNKI:SUN:CGJS.0.2021-07-020
  • Huang H, Luo B. A received signal strength indication adaptive algorithm for wireless sensor network. Appl Mech Mater. 2013; 273: 505–509. https://doi.org/10.4028/www.scientific.net/amm.273.505.
  • Shi XH, Ni YX. Indoor staff Kalman filter location algorithm based on RSSI. J Xi‘An Univ Sci Technol. 2020;40(1):167–172. doi: 10.13800/j.cnki.xakjdxxb.2020.0122
  • Yang T, Cabani A, Chafouk H. A survey of Recent indoor localization scenarios and Methodologies. Sens. 2021;21(23):8086. doi: 10.3390/s21238086
  • Labinghisa BA, Lee DM. Neural network-based indoor localization system with enhanced virtual access points. J Supercomput. 2021;77(1):638–651. doi: 10.1007/s11227-020-03272-4
  • Wang JQ, Dai Y, Lei QF. Indoor localization algorithm of RSSI filtering by Dixon test. J Sens Technol. 2021;34(1):118–123. CNKI:SUN:CGJS.0.2021-01-018
  • Cheng J. Trilateral indoor location algorithm based on RSSI filtering. Sci-Tech Wind. 2019(28):115+122. doi:10.19392/j.cnki.1671-7341.201928094.
  • Wang ZW, Fang L. Weighted Collaborative localization algorithm based on fuzzy clustering. J Shangluo Univ. 2022;36(6):54–58. doi: 10.13440/j.slxy.1674-0033.2022.06.009
  • Zhu QY, Ma XZ. Improved clustering algorithm for trilateration in wireless sensor networks. J Shandong Agric Univ. 2019;50(3):473–476.
  • Liu X. Research on indoor fingerprint map location algorithm based on RSSI [MA thesis]. North Univ China. 2019. https://kns.cnki.net/KCMS/detail/detail.aspx?dbname=CMFD201902&filename=1019098806.nh.
  • Lee SG, Lee C Developing an improved fingerprint positioning radio map using the k-means clustering algorithm//2020 International Conference on Information Networking (ICOIN). IEEE, 2020: 761–765.doi:10.1109/ICOIN48656.2020.9016627.
  • Hu S, Shen C, Zhang K, et al. Improved Wknn indoor positioning algorithm based on C-Means and Chi-square Distance[C]//2019. International Conference on Robots & Intelligent System (ICRIS); Haikou, China. IEEE; 2019. p. 432–435. doi:10.1109/ICRIS.2019.00113
  • Huang YG, Zhang SS, Liu HJ. Traffic state classification based on Gaussian mixture model clustering algorithm. Mod Electron Technol. 2022;45(7):168–173. doi: 10.16652/j.issn.1004-373x.2022.07.031
  • Wang ZH. Improvement of prison personnel location algorithm based on RSSI ranging. Mod Inf Technol. 2021;5(15):36–39. doi: 10.19850/j.cnki.2096-4706.2021.15.010
  • Du JX, Chen YW, Zhang J. Distance correction indoor positioning algorithm based on cluster analysis optimization. Comput Eng Sci. 2018;40(2):246–254.
  • Nomura H, Ichikawa H, Kawawkita Y Reference node selection for range -based localization using hierarchical clustering//2018 IEEE 4th world Forum on internet of things(WF-IoT). IEEE, 2018 : 140–143. doi:10.1109/WF-IoT.2018.8355228.
  • Wang ZW, Fang L. Weighted Collaborative localization algorithm based on fuzzy clustering. J Shangluo Universit. 2022;36(6):54–58. doi: 10.13440/j.slxy.1674-0033.2022.06.009
  • Lan QQ, Xiao B. RFID location based on grid density peak clustering algorithm. J Electron Meas Instrum. 2018;32(10):73–78. doi: 10.13382/j.jemi.2018.10.011
  • Mao KJ, Wu JB, Jin HB. Indoor location algorithm for NLOS environment. J Electron. 2016;44(5):1174–1179.
  • Rodriguez A, Liao A. Clustering by fast search and find of density peaks. Sci. 2014;344(6191):1492–1496. doi: 10.1126/science.1242072
  • Gao SY. Research on subarea clustering indoor location algorithm based on improved affinity propagation clustering algorithm. Taiyuan: Taiyuan University of Technology; 2019. pp. 1–64.
  • Hao Y, Du Z, Xing Z, et al. Urban hazardous chemicals pipeline leakage positioning method based on CELMD-MCKD. Case Stud Nondestr Test Eval. 2021;36(5):477–493. doi: 10.1080/10589759.2020.1803860
  • Ouyang RW, Wong AKS, Woo KT. Indoor localization via discriminatively regularized least square classification. Int J Wireless Inf Network. 2011;18(2):57–72. doi: 10.1007/s10776-011-0133-5
  • Sengur A. Multiclass least-squares support vector machines for analog modulation classification. Expert Syst Appl. 2009;36(3, Part 2):6681–6685. doi: 10.1016/j.eswa.2008.08.066
  • Graves A. Generating sequences with recurrent neural networks. arXiv Preprint arXiv: 1308.0850. 2013. doi: 10.48550/arXiv.1308.0850
  • Jozefowicz R, Zaremba W, Sutskever I. An empirical exploration of recurrent network architectures. In International conference on machine learning. PMLR. 2015;2342–2350.
  • Kuo R-J, Chen C, Liao TW, et al. Hybrid of artificial immune system and particle swarm optimization-based support vector machine for radio frequency identification-based positioning system. Comput Ind Eng. 2013;64(1):333–341. doi: 10.1016/j.cie.2012.10.007
  • Torres-Sospedra J, Moreira A, Knauth S, et al. A realistic evaluation of indoor positioning systems based on Wi-Fi fingerprinting: The 2015 EvAAL–ETRI competition. J Ambient Intell Smart Environ. 2017;9(2):263–279. doi: 10.3233/AIS-170421
  • Jiangtao Z, Honglan W An improved WKNN algorithm using in indoor positioning. 2019 6th International Conference on Information Science and Control Engineering (ICISCE). IEEE, 2019: 136–140. doi:10.1109/ICISCE48695.2019.00037
  • Echizenya K, Kondo K, editors Comparison of RSSI processing methods for improved estimation accuracy in BLE indoor position and Movement direction estimation system using DNN. 2021 IEEE 10th Global Conference on Consumer Electronics (GCCE); 2021 Oct 12-15; 2021. doi:10.1109/GCCE53005.2021.9622021
  • Oh SH, Kim JG, editors. DNN based WiFi positioning in 3GPP indoor office environment. 2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC); 2021 Apr 13-16; 2021.doi:10.1109/ICAIIC51459.2021.9415207
  • Golenbiewski J, Tewolde G Implementation of an indoor positioning system using the WKNN Algorithm. 2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC). IEEE, 2019: 0397–0400.doi: 10.1109/CCWC.2019.8666476.
  • Gong ZX. Sectional fingerprint location based on CSI. Huazhong University of Science and Technology; 2020. doi: 10.27157/d.cnki.ghzku.2020.000446.
  • Elbes M, Almaita E, Alrawashdeh T, et al. An indoor localization approach based on deep learning for indoor location-based services. 2019 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT). IEEE, 2019: 437–441. doi:10.1109/JEEIT.2019.8717509.
  • Jiang C, Shen J, Chen S, et al. UWB NLOS/LOS classification using deep learning method. IEEE Commun Lett. 2020;24(10):2226–2230. doi: 10.1109/LCOMM.2020.2999904
  • Jang HJ, Shin JM, Choi L, editors Geomagnetic field based indoor localization using recurrent neural networks. GLOBECOM 2017-2017 IEEE Global Communications Conference; 2017: IEEE.doi:10.1109/GLOCOM.2017.8254556
  • Pal A, Kundu T, Datta AK. Damage localization in Rail Section using single AE sensor data: An experimental Investigation with deep learning approach. Case Stud Nondestr Test Eval. 2023;1–19. doi: 10.1080/10589759.2023.2243004
  • Jiang YH. Indoor fingerprint location algorithm based on singular value detection and AP clustering. Comput Eng Des. 2015;36(11):3010–3013. doi: 10.16208/j.issn1000-7024.2015.11.026
  • Mao YY, Lv D. Optimization of fingerprint location algorithm based on AP-SVM hybrid classification. Sen Microsys. 2022;41(7):126–129+138. doi: 10.13873/J.1000-9787(2022)07-0126-04
  • Fan JC, Deng ZL, Jiao JC DBSCAN-SVM fusion clustering algorithm based on location fingerprint. Proceedings of the 8th China Satellite Navigation Academic Annual Conference-S10 Multi-source fusion navigation technology; Shanghai,China. 2017. p. 36–39. https://kns.cnki.net/kcms2/article/abstract?v=lDUDaiPi9qeCSIZ8ZruGoMTpUWqWn02N7MBsDvVGA-xkxVy_CnwVYP-YNulSmaOE4Hrd0EzlVEUdg0KRVj0W_dvzHoBOOSwwyw_K_mupgG4T7CtaUfoQZOSOjoyZfzW8xxIOY9iL4R4=&uniplatform=NZKPT&language=CHS.
  • Hoang MT, Yuen B, Dong X, et al. Recurrent neural networks for accurate RSSI indoor localization. IEEE Int Things J. 2019;6(6):10639–10651. doi: 10.1109/JIOT.2019.2940368
  • Qin F, Zuo T, Wang X. Ccpos: Wifi fingerprint indoor positioning system based on cdae-cnn. Sens. 2021;21(4):1114. doi: 10.3390/s21041114
  • Ferreira BV, Carvalho E, Ferreira MR, et al. Exploiting the use of convolutional neural networks for localization in indoor environments. Appl Artif Intell. 2017;31(3):279–287. doi: 10.1080/08839514.2017.1316592
  • Yang Z, Huo L. Bolt preload monitoring based on percussion sound signal and convolutional neural network (CNN). Case Stud Nondestr Test Eval. 2022;37(4):464–481. doi: 10.1080/10589759.2022.2030735
  • Peng C, Jiang H, Qu L. Deep convolutional neural network for passive RFID tag localization via joint RSSI and PDOA fingerprint features. IEEE Access. 2021;9:15441–15451. doi: 10.1109/ACCESS.2021.3052567
  • Soro B, Lee C. A wavelet scattering feature extraction approach for deep neural network based indoor fingerprinting localization. Sens. 2019;19(8):1790. doi: 10.3390/s19081790
  • Chen W, Chang Q, Hou H, et al. A novel clustering and KWNN-based strategy for WiFi fingerprint indoor location. In: 2015 4th International Conference on Computer Science and Network Technology(ICCSNT). IEEE, 2015, 1 : 46–52.doi:10.1109/ICCSNT.2015.7490706.
  • Li ZH, Huang JS. GPS surveying and data processing. Wuhan: Wuhan University Press; 2005.
  • Brack A. Reliable GPS + BDS RTK positioning with partial ambiguity resolution. GPS Solution. 2016;21(3):1083–1092. doi: 10.1007/s10291-016-0594-1 2016/12/24.
  • Zhang AG, Wu QY. Research on indoor positioning of RSSI fingerprint database with differential correction. J Shandong Univ Sci Technol. 2020;39(3):24–32. doi: 10.16452/j.cnki.sdkjzk.2020.03.003
  • Liu Z, Fu Z, Li T, et al. A phase and RSSI-Based method for indoor localization using passive RFID system with mobile Platform. IEEE J Radio Freq Identif. 2022;6:544–551. doi: 10.1109/JRFID.2022.3179620
  • Tanbo M, Nojiri R, Kawakita Y, et al., editors Active RFID attached object clustering method based on RSSI series for finding lost objects. 2015 IEEE 2nd World Forum on Internet of Things (WF-IoT); 2015 Dec 14-16; 2015.doi:10.1109/WF-IoT.2015.7389081
  • Llorca DF, Quintero R, Parra I, et al., editors. Fusing directional passive UHF RFID and stereo vision for tag association in outdoor scenarios. 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC); 2016 Nov 1-4; 2016.doi:10.1109/ITSC.2016.7795962
  • Hadj-Mihoub-Sidi-Moussa H, Tedjini S, Touhami R, editors Phase selector for RFID localization system based on RSSI filter. 2019 14th International Conference on Design & Technology of Integrated Systems In Nanoscale Era (DTIS); 2019 Apr 16-18; 2019.doi:10.1109/DTIS.2019.8735016
  • In: Zhao J, Zhang Y, Ye M, editors. Research on the received signal strength indication location algorithm for RFID system. 2006 International Symposium on Communications and Information Technologies. 2006 Oct 18-20; 2006: doi:10.1109/ISCIT.2006.339863
  • Xu B, Zhu X, Zhu H. An efficient indoor localization method based on the long short-term memory recurrent Neuron network. IEEE Access. 2019;7:123912–123921. doi: 10.1109/ACCESS.2019.2937831
  • Zhong Z, Tang Z, Li X, et al., editors. XJTLUIndoorLoc: A New fingerprinting database for indoor localization and Trajectory estimation based on Wi-Fi RSS and Geomagnetic field. 2018 Sixth International Symposium on Computing and Networking Workshops (CANDARW); 2018 Nov 27-30; 2018.doi:10.1109/CANDARW.2018.00050
  • Chen Z, Zou H, Yang J, et al. WiFi fingerprinting indoor localization using local feature-based deep LSTM. IEEE Syst J. 2020;14(2):3001–3010. doi: 10.1109/JSYST.2019.2918678
  • Dai P, Yang Y, Wang M, et al. Combination of DNN and improved KNN for indoor location fingerprinting. Wireless Commun Mobile Comput. 2019;2019. doi: 10.1155/2019/4283857.
  • Iqbal Z, Luo D, Henry P, et al. Accurate real time localization tracking in a clinical environment using Bluetooth Low Energy and deep learning. PLoS One. 2018;13(10):e0205392. doi: 10.1371/journal.pone.0205392
  • Oh SH, Kim JG, editors VLC Positioning by DNN via WkNN in Indoor Environment. 2022 Thirteenth International Conference on Ubiquitous and Future Networks (ICUFN); 2022 Jul 5-8; 2022.doi:10.1109/ICUFN55119.2022.9829653
  • Wang H, Wang X, Xue Y, et al., editors UWB-based indoor localization using a Hybrid WKNN-LSTM algorithm. 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC); 2020 Jun 12-14; 2020.doi:10.1109/ITNEC48623.2020.9085050
  • Oh SH, Kim JG, editors DNN-based Positioning with Optimum Input Parameter in Indoor VLC LOS/NLOS Environment. 2022 27th Asia Pacific Conference on Communications (APCC); 2022 Oct 19-21; 2022.doi:10.1109/APCC55198.2022.9943663
  • Pei Y, Chen R, Li D, et al. FCN-Attention: A deep learning UWB NLOS/LOS classification algorithm using fully convolution neural network with self-attention mechanism. Geo Spatial Inf Sci. 2023;1–20. doi: 10.1080/10095020.2023.2178334
  • Purusothaman P, Gopalakrishnan B. Enhanced localization model in wireless sensor network using Self adaptive-Barnacles Mating optimization. Cybern Syst. 2022. doi: 10.1080/01969722.2022.2137622
  • Bencharif L, Ouameur MA, Massicotte D, editors. Long Short-Term Memory for Indoor Localization Using WI-FI Received Signal Strength and Channel State Information. 2021 IEEE 4th 5G World Forum (5GWF); 2021 Oct 13-15; 2021.doi:10.1109/5GWF52925.2021.00047
  • Kim KS, Lee S, Huang K. A scalable deep neural network architecture for multi-building and multi-floor indoor localization based on Wi-Fi fingerprinting. Big Data Anal. 2018;3(1):1–17. doi: 10.1186/s41044-018-0031-2
  • Yu D, Li J. Recent progresses in deep learning based acoustic models. IEEE/CAA J Autom Sin. 2017;4(3):396–409. doi: 10.1109/JAS.2017.7510508
  • Zhang W, Liu K, Zhang W, et al. Deep neural networks for wireless localization in indoor and outdoor environments. Neurocomput. 2016;194:279–287. doi: 10.1016/j.neucom.2016.02.055
  • Dong Q, Dargie W, editors Evaluation of the reliability of RSSI for indoor localization. 2012 International Conference on Wireless Communications in Underground and Confined Areas; 2012: IEEE. doi:10.1109/ICWCUCA.2012.6402492
  • Bae Y. Robust localization for robot and IoT using RSSI. Energies. 2019;12(11):2212. doi: 10.3390/en12112212
  • Xue W, Qiu W, Hua X, et al. Improved Wi-Fi RSSI measurement for indoor localization. IEEE Sens J. 2017;17(7):2224–2230. doi: 10.1109/JSEN.2017.2660522
  • Pallares-Calvo AE, Carvajal-Gámez BE, Gutiérrez-Frías O, et al. Analysis of data Reception in the Communication layer applied to an Architecture of mobile sensor networks in Marine environments. Sens. 2023;23(12):5480. doi: 10.3390/s23125480
  • Straka T, Vojtech L, Neruda M. Simulation of radio signal propagation for UHF RFID technology in an indoor environment using Ray Tracing (Graphics) method. Appl Sci. 2022;12(21):11065. doi: 10.3390/app122111065
  • Wang X, Ding H, Luo Z, et al. The indoor positioning method time difference of Arrival with Conic Curves utilizing a Novel Networking RFID system. Electron. 2023;12(15):3236. doi: 10.3390/electronics12153236
  • Jarašūnienė A, Čižiūnienė K, Čereška A. Research on impact of IoT on Warehouse management. Sens. 2023;23(4):2213. doi: 10.3390/s23042213
  • Olesiński A, Piotrowski Z. An adaptive Energy saving algorithm for an RSSI-Based localization system in mobile radio Sensors. Sens. 2021;21(12):3987. doi: 10.3390/s21123987
  • Guo Y, Chen G, Katagiri T, et al. Quantitative evaluation of pipe wall thinning defect sizes using microwave NDT. Case Stud Nondestr Test Eval. 2022;37(6):737–753. doi: 10.1080/10589759.2022.2051505
  • Li H, Trocan M, Galayko D. Virtual fingerprint and two-way ranging-based Bluetooth 3D indoor positioning with RSSI difference and distance ratio. J Electromagn Waves Appl. 2019;33(16):2155–2174. doi: 10.1080/09205071.2019.1667268
  • Li Z, Haigh A, Soutis C, et al. A review of microwave testing of glass fibre-reinforced polymer composites. Case Stud Nondestr Test Eval. 2019;34(4):429–458. doi: 10.1080/10589759.2019.1605603
  • Chen Q, Ding D, Zheng Y. Indoor pedestrian tracking with sparse RSS fingerprints. Tsinghua Sci Technol. 2018;23(1):95–103. doi: 10.26599/tst.2018.9010026
  • Zhao Y, Zhou H, Chen Y. Application of Kalman filter in real-time tracking of indoor positioning system. J Wuhan Univ Sci Edn. 2009;55(6):696–700.
  • Zhou ZH. Machine learning. Beijing, China: Tsinghua University Publishing House; 2016. p. 130–160.
  • Xu W, Song Z, Sun Y, et al. Capture-Aware dense tag identification using RFID systems in Vehicular networks. Sens. 2023;23(15):6792. doi: 10.3390/s23156792
  • Decker L, Zoghi B. The Case for RFID-Enabled Traceability in Cash Movements. FinTech. 2023;2(2):344–373. doi: 10.3390/fintech2020020
  • Wilczkiewicz B, Jankowski-Mihułowicz P, Węglarski M. Test Platform for Developing Processes of Autonomous identification in RFID systems with Proximity-range Read/Write Devices. Electron. 2023;12(3):617. doi: 10.3390/electronics12030617
  • Benes F, Stasa P, Svub J, et al. Investigation of UHF signal strength propagation at Warehouse management applications based on Drones and RFID technology Utilization. Appl Sci. 2022;12(3):1277. doi: 10.3390/app12031277
  • Li Y, Ma Y, Tian C, et al. DNCL: Hybrid DOA estimation and NMDS Cooperative multi-target localization for RFID. Electron. 2023;12(7):1742. doi: 10.3390/electronics12071742
  • Tao Z, Wang HY. An improved WLAN indoor location algorithm based on chi-square distanc. Comput Technol Dev. 2016;26(9):50–55.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.