# High-Precision RTT-Based Indoor Positioning System Using RCDN and RPN

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## Abstract

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## 1. Introduction of Indoor Positioning System Methods

## 2. Related Works

#### 2.1. Positioning System based on Neural Networks

#### 2.2. RTT-Based Wi-Fi Positioning

## 3. Proposed High-Precision RTT-Based Indoor Positioning System

#### 3.1. Proposed RTT-Based Indoor Positioning System

#### 3.2. Proposed RTT Compensation Distance Network (RCDN)

#### 3.3. Proposed RTT Positioning Network (RPN)

## 4. Experiment Results and Discussion

#### 4.1. Experimental Environment and RTT Configuration

#### 4.2. Learning Results of Proposed Positioning Network

#### 4.3. Positioning Performance Evaluation

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

- Jiménez, A.R.; Seco, F. Improving the Accuracy of Decawave’s UWB MDEK1001 Location System by Gaining Access to Multiple Ranges. Sensors
**2021**, 21, 1787. [Google Scholar] [CrossRef] - Frankó, A.; Vida, G.; Varga, P. Reliable identification schemes for asset and production tracking in industry 4.0. Sensors
**2020**, 20, 3709. [Google Scholar] [CrossRef] [PubMed] - Jiang, C.; Xu, B.; Hsu, L.T. Probabilistic approach to detect and correct GNSS NLOS signals using an augmented state vector in the extended Kalman filter. GPS Solut.
**2021**, 25, 1–14. [Google Scholar] [CrossRef] - Pardhasaradhi, B.; Srihari, P.; Aparna, P. Navigation in GPS Spoofed Environment Using M-Best Positioning Algorithm and Data Association. IEEE Access
**2021**, 9, 51536–51549. [Google Scholar] [CrossRef] - Xiang, C.; Zhang, S.; Xu, S.; Alexandropoulos, G.C. Self-Calibrating Indoor Localization with Crowdsourcing Fingerprints and Transfer Learning. arXiv Prepr.
**2021**, arXiv:2101.10527. [Google Scholar] - Alhomayani, F.; Mahoor, M.H. Deep learning methods for fingerprint-based indoor positioning: A review. J. Locat. Based Serv.
**2020**, 14, 129–200. [Google Scholar] [CrossRef] - Seong, J.H.; Seo, D.H. Wi-Fi fingerprint using radio map model based on MDLP and Euclidean distance based on the Chi squared test. Wirel. Netw.
**2019**, 25, 3019–3027. [Google Scholar] [CrossRef] - Ssekidde, P.; Steven Eyobu, O.; Han, D.S.; Oyana, T.J. Augmented CWT Features for Deep Learning-Based Indoor Localization Using WiFi RSSI Data. Appl. Sci.
**2021**, 11, 1806. [Google Scholar] [CrossRef] - Sun, H.; Zhu, X.; Liu, Y.; Liu, W. Construction of Hybrid Dual Radio Frequency RSSI (HDRF-RSSI) Fingerprint Database and Indoor Location Method. Sensors
**2020**, 20, 2981. [Google Scholar] [CrossRef] - Wang, J.; Park, J.G. An enhanced indoor ranging method using CSI measurements with Extended Kalman filter. In Proceedings of the 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS), Portland, OR, USA, 20–23 April 2020; pp. 1342–1348. [Google Scholar]
- Dang, X.; Tang, X.; Hao, Z.; Ren, J. Discrete Hopfield neural network based indoor Wi-Fi localization using CSI. EURASIP J. Wirel. Commun. Netw.
**2020**, 76. [Google Scholar] [CrossRef] - Bai, L.; Ciravegna, F.; Bond, R.; Mulvenna, M. A low cost indoor positioning system using Bluetooth low energy. IEEE Access
**2020**, 8, 136858–136871. [Google Scholar] [CrossRef] - Ho, Y.H.; Chan, H.C. Decentralized adaptive indoor positioning protocol using Bluetooth Low Energy. Comput. Commun.
**2020**, 159, 231–244. [Google Scholar] [CrossRef] - Seong, J.H.; Lee, S.H.; Yoon, K.K.; Seo, D.H. Ellipse coefficient map-based geomagnetic fingerprint considering azimuth angles. Symmetry
**2019**, 11, 708. [Google Scholar] [CrossRef] [Green Version] - Uradzinski, M.; Guo, H.; Liu, X.; Yu, M. Advanced indoor positioning using zigbee wireless technology. Wirel. Pers. Commun.
**2017**, 97, 6509–6518. [Google Scholar] [CrossRef] - Zhen, J.; Liu, B.; Wang, Y.; Liu, Y. An improved method for indoor positioning based on ZigBee technique. Int. J. Embed. Syst.
**2020**, 13, 292–299. [Google Scholar] [CrossRef] - Yang, Y.; Wang, M.; Qiao, Y.; Zhang, B.; Yang, H. Efficient marginalized particle smoother for indoor CSS–TOF localization with non-Gaussian errors. Remote Sens.
**2020**, 12, 3838. [Google Scholar] [CrossRef] - An, Z.; Lin, Q.; Yang, L.; Guo, Y. Revitalizing Ultrasonic Positioning Systems for Ultrasound-Incapable Smart Devices. IEEE Trans. Mob. Comput.
**2020**, 20, 2007–2024. [Google Scholar] [CrossRef] - Feng, D.; Wang, C.; He, C.; Zhuang, Y.; Xia, X.G. Kalman-filter-based integration of IMU and UWB for high-accuracy indoor positioning and navigation. IEEE Internet Things J.
**2020**, 7, 3133–3146. [Google Scholar] [CrossRef] - Zhang, Y.; Duan, L. Toward elderly care: A phase-difference-of-arrival assisted ultra-wideband positioning method in smart home. IEEE Access
**2020**, 8, 139387–139395. [Google Scholar] [CrossRef] - Zhang, H.; Zhang, Z.; Gao, N.; Xiao, Y.; Meng, Z.; Li, Z. Cost-effective wearable indoor localization and motion analysis via the integration of UWB and IMU. Sensors
**2020**, 20, 344. [Google Scholar] [CrossRef] [PubMed] [Green Version] - De Angelis, G.; Moschitta, A.; Carbone, P. Positioning techniques in indoor environments based on stochastic modeling of UWB round-trip-time measurements. IEEE Trans. Intell. Transp. Syst.
**2016**, 17, 2272–2281. [Google Scholar] [CrossRef] - Martinelli, A.; Jayousi, S.; Caputo, S.; Mucchi, L. UWB positioning for industrial applications: The galvanic plating case study. In Proceedings of the 2019 International Conference on Indoor Positioning and Indoor Navigation (IPIN), Pisa, Italy, 30 September–3 October 2019; pp. 1–7. [Google Scholar]
- Martinelli, A.; Dolfi, M.; Morosi, S.; Mucchi, L.; Paoli, M.; Agili, A. Ultra-wide Band Positioning in Sport: How the Relative Height Between the Transmitting and the Receiving Antenna Affects the System Performance. Int. J. Wirel. Inf. Netw.
**2020**, 27, 18–29. [Google Scholar] [CrossRef] - Sun, M.; Wang, Y.; Xu, S.; Qi, H.; Hu, X. Indoor positioning tightly coupled Wi-Fi FTM ranging and PDR based on the extended Kalman filter for smartphones. IEEE Access
**2020**, 8, 49671–49684. [Google Scholar] [CrossRef] - Shao, W.; Luo, H.; Zhao, F.; Tian, H.; Yan, S.; Crivello, A. Accurate indoor positioning using temporal-spatial constraints based on Wi-Fi fine time measurements. IEEE Internet Things J.
**2020**, 7, 11006–11019. [Google Scholar] [CrossRef] - Fang, X.; Chen, L. An optimal multi-channel trilateration localization algorithm by radio-multipath multi-objective evolution in RSS-ranging-based wireless sensor networks. Sensors
**2020**, 20, 1798. [Google Scholar] [CrossRef] [Green Version] - Yang, B.; Guo, L.; Guo, R.; Zhao, M.; Zhao, T. A novel trilateration algorithm for RSSI-based indoor localization. IEEE Sens. J.
**2020**, 20, 8164–8172. [Google Scholar] [CrossRef] - Shi, Y.; Shi, W.; Liu, X.; Xiao, X. An RSSI Classification and Tracing Algorithm to Improve Trilateration-Based Positioning. Sensors
**2020**, 20, 4244. [Google Scholar] [CrossRef] - Cao, H.; Wang, Y.; Bi, J.; Xu, S.; Si, M.; Qi, H. Indoor Positioning Method Using WiFi RTT Based on LOS Identification and Range Calibration. ISPRS Int. J. Geo-Inf.
**2020**, 9, 627. [Google Scholar] [CrossRef] - Ma, C.; Wu, B.; Poslad, S.; Selviah, D.R. Wi-Fi RTT Ranging Performance Characterization and Positioning System Design. IEEE Trans. Mob. Comput.
**2020**. [Google Scholar] [CrossRef] - Markus, B.; Toni, F.; Frank, E.; Markus, E.; Frank, D.; Marcin, G. Comparison of 2.4 GHz WiFi FTM- and RSSI-Based Indoor Positioning Methods in Realistic Scenarios. Sensors
**2020**, 20, 4515. [Google Scholar] - Horn, B.K. Doubling the Accuracy of Indoor Positioning: Frequency Diversity. Sensors
**2020**, 20, 1489. [Google Scholar] [CrossRef] [Green Version] - Gentner, C.; Ulmschneider, M.; Kuehner, I.; Dammann, A. WiFi-RTT Indoor Positioning. In Proceedings of the 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS), Portland, OR, USA, 20–23 April 2020; pp. 1029–1035. [Google Scholar]
- Huang, L.; Yu, B.; Li, H.; Zhang, H.; Li, S.; Zhu, R.; Li, Y. HPIPS: A high-precision indoor pedestrian positioning system fusing WiFi-RTT, MEMS, and map information. Sensors
**2020**, 20, 6795. [Google Scholar] [CrossRef] [PubMed] - Seong, J.H.; Seo, D.H. Selective unsupervised learning-based Wi-Fi fingerprint system using autoencoder and GAN. IEEE Internet Things J.
**2020**, 7, 1898–1909. [Google Scholar] [CrossRef] - Hsieh, C.H.; Chen, J.Y.; Nien, B.H. Deep learning-based indoor localization using received signal strength and channel state information. IEEE Access
**2019**, 7, 33256–33267. [Google Scholar] [CrossRef] - Wang, X.; Wang, X.; Mao, S. Deep convolutional neural networks for indoor localization with CSI images. IEEE Trans. Netw. Sci. Eng.
**2018**, 7, 316–327. [Google Scholar] [CrossRef] - Cui, Z.; Gao, Y.; Hu, J.; Tian, S.; Cheng, J. LOS/NLOS identification for indoor UWB positioning based on Morlet wavelet transform and convolutional neural networks. IEEE Commun. Lett.
**2020**, 25, 879–882. [Google Scholar] [CrossRef] - Nguyen, D.T.A.; Lee, H.G.; Jeong, E.R.; Lee, H.L.; Joung, J. Deep learning-based localization for UWB systems. Electronics
**2020**, 9, 1712. [Google Scholar] [CrossRef] - Zhang, Y.; Xiong, R.; He, H.; Pecht, M.G. Long short-term memory recurrent neural network for remaining useful life prediction of lithium-ion batteries. IEEE Trans. Veh. Technol.
**2018**, 67, 5695–5705. [Google Scholar] [CrossRef] - Wu, L.; Chen, C.H.; Zhang, Q. A mobile positioning method based on deep learning techniques. Electronics
**2019**, 8, 59. [Google Scholar] [CrossRef] [Green Version] - Tarekegn, G.B.; Juang, R.T.; Lin, H.P.; Adege, A.B.; Munaye, Y.Y. DFOPS: Deep learning-based fingerprinting outdoor positioning scheme in hybrid networks. IEEE Internet Things J.
**2020**, 8, 3717–3729. [Google Scholar] [CrossRef] - Sun, H.; Zhu, X.; Liu, Y.; Liu, W. WiFi based fingerprinting positioning based on Seq2seq model. Sensors
**2020**, 20, 3767. [Google Scholar] [CrossRef] [PubMed] - Hoang, M.T.; Yuen, B.; Dong, X.; Lu, T.; Westendorp, R.; Reddy, K. Recurrent neural networks for accurate RSSI indoor localization. IEEE Internet Things J.
**2019**, 6, 10639–10651. [Google Scholar] [CrossRef] [Green Version]

Compensation Method | Standard Score | Only RCDN | Proposed Network (End-to-End) |
---|---|---|---|

Ranging error (m) | 0.93 | 0.81 | 0.60 |

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**MDPI and ACS Style**

Seong, J.-H.; Lee, S.-H.; Kim, W.-Y.; Seo, D.-H.
High-Precision RTT-Based Indoor Positioning System Using RCDN and RPN. *Sensors* **2021**, *21*, 3701.
https://doi.org/10.3390/s21113701

**AMA Style**

Seong J-H, Lee S-H, Kim W-Y, Seo D-H.
High-Precision RTT-Based Indoor Positioning System Using RCDN and RPN. *Sensors*. 2021; 21(11):3701.
https://doi.org/10.3390/s21113701

**Chicago/Turabian Style**

Seong, Ju-Hyeon, Soo-Hwan Lee, Won-Yeol Kim, and Dong-Hoan Seo.
2021. "High-Precision RTT-Based Indoor Positioning System Using RCDN and RPN" *Sensors* 21, no. 11: 3701.
https://doi.org/10.3390/s21113701