# Calibration of Wi-Fi-Based Indoor Tracking Systems for Android-Based Smartphones

^{1}

^{2}

^{*}

## Abstract

**:**

## 1. Introduction

- The analysis of the RSSI signal behavior of in-motion captures in order to implement an indoor tracking system for smartphones.
- The proposal of a novel in-motion calibration methodology, which can be implemented in a typical indoor scenario using an inexpensive time procedure. The outcome is a signal propagation representation of the access points belonging to the wireless platform.
- The design of an indoor tracking system following a particle filter approach. In particular, instead of converting the RSSI into distance, the weighting stage of the particle filter algorithm makes use of the RSSI estimation according to the distance. That RSSI estimation is based on the signal propagation representation adopted.

## 2. Related Work

#### 2.1. Factors Affecting Wi-Fi Signal

#### 2.2. Calibration of Wi-Fi-Based Indoor Localization Systems

## 3. Study of Wi-Fi Signal Behavior in Motion

#### 3.1. RSSI Smoothing

#### 3.2. Relation between RSSI and Distance

#### 3.3. Path Loss Exponent Evolution

- A smoothing algorithm needs to be used in order to reduce the inherent Wi-Fi signal noise.
- The in-motion RSSI value evolution shows a fading-relation with distance. However, the fading exhibits a different behavior depending upon the distance and LOS condition between the transmitter and the receiver.
- Different path loss exponent values are needed to be used according to the distance between transmitter and receiver devices.

## 4. Calibration Methodologies for Smartphone-Based Indoor Tracking Systems

#### 4.1. Static Calibration Methodology

#### 4.1.1. Static One Representation

#### 4.1.2. Static Zone Representation

#### 4.2. In-Motion Calibration Methodology

#### 4.2.1. In-Motion One Representation

#### 4.2.2. In-Motion Zone Representation

#### 4.2.3. In-Motion Grid Representation

## 5. Particle-Filter-Based Indoor Tracking Algorithm

#### 5.1. Initialization

#### 5.2. Measurement Update

#### 5.3. Current State Estimation

#### 5.4. Resampling

#### 5.5. Motion Update

## 6. Experimental Results

#### 6.1. Environment Definition

#### 6.2. Experimental Setup

#### 6.3. Comparison of Signal Propagation Representations

#### 6.4. Tracking Accuracy

## 7. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## References

- Chen, Z.; Zou, H.; Jiang, H.; Zhu, Q.; Soh, Y.; Xie, L. Fusion of WiFi, smartphone sensors and landmarks using the Kalman filter for indoor localization. Sensors
**2015**, 15, 715–732. [Google Scholar] [CrossRef] [PubMed] - Sun, G.; Chen, J.; Guo, W.; Liu, K. Signal processing techniques in network-aided positioning: A survey of state-of-the-art positioning designs. IEEE Signal Process. Mag.
**2005**, 22, 12–23. [Google Scholar] - Sadowski, S.; Spachos, P. RSSI-Based Indoor Localization With the Internet of Things. IEEE Access
**2018**, 6, 30149–30161. [Google Scholar] [CrossRef] - Spachos, P.; Papapanagiotou, I.; Plataniotis, K.N. Microlocation for Smart Buildings in the Era of the Internet of Things: A Survey of Technologies, Techniques, and Approaches. IEEE Signal Process. Mag.
**2018**, 35, 140–152. [Google Scholar] [CrossRef] - Hashemi, H. The indoor radio propagation channel. Proc. IEEE
**1993**, 81, 943–968. [Google Scholar] [CrossRef] - Castillo-Cara, M.; Lovón-Melgarejo, J.; Bravo-Rocca, G.; Orozco-Barbosa, L.; García-Varea, I. An empirical study of the transmission power setting for bluetooth-based indoor localization mechanisms. Sensors
**2017**, 17, 1318. [Google Scholar] [CrossRef] [PubMed] - Rappaport, T. Wireless Communications, Principles and Practices; Prentice Hall: Upper Saddle River, NJ, USA, 1996. [Google Scholar]
- Martínez-Gómez, J.; Martinez del Horno, M.; Castillo-Cara, M.; Brea Lujan, V.M.; Barbosa, L.O.; García-Varea, I. Spatial statistical analysis for the design of indoor particle-filter-based localization mechanisms. Int. J. Distrib. Sens. Netw.
**2016**, 12. [Google Scholar] [CrossRef] - Zhou, G.; He, T.; Krishnamurthy, S.; Stankovic, J.A. Models and solutions for radio irregularity in wireless sensor networks. ACM Trans. Sens. Netw. (TOSN)
**2006**, 2, 221–262. [Google Scholar] [CrossRef] - Zhang, R.B.; Guo, J.G.; Chu, F.H.; Zhang, Y.C. Environmental-adaptive indoor radio path loss model for wireless sensor networks localization. AEU-Int. J. Electron. Commun.
**2011**, 65, 1023–1031. [Google Scholar] [CrossRef] - Belmonte-Hernández, A.; Hernández-Peñaloza, G.; Álvarez, F.; Conti, G. Adaptive fingerprinting in multi-sensor fusion for accurate indoor tracking. IEEE Sens. J.
**2017**, 17, 4983–4998. [Google Scholar] [CrossRef] - Chen, J.; Zhang, Y.; Xue, W. Unsupervised Indoor Localization Based on Smartphone Sensors, iBeacon and Wi-Fi. Sensors
**2018**, 18, 1378. [Google Scholar] [CrossRef] [PubMed] - Booranawong, A.; Sengchuai, K.; Jindapetch, N. Implementation and test of an RSSI-based indoor target localization system: Human movement effects on the accuracy. Measurement
**2019**, 133, 370–382. [Google Scholar] [CrossRef] - Zou, H.; Lu, X.; Jiang, H.; Xie, L. A fast and precise indoor localization algorithm based on an online sequential extreme learning machine. Sensors
**2015**, 15, 1804–1824. [Google Scholar] [CrossRef] - Du, J.; Diouris, J.F.; Wang, Y. A RSSI-based parameter tracking strategy for constrained position localization. EURASIP J. Adv. Signal Process.
**2017**, 2017, 77. [Google Scholar] [CrossRef] - Lott, M.; Forkel, I. A multi-wall-and-floor model for indoor radio propagation. In Proceedings of the IEEE VTS 53rd Vehicular Technology Conference, Spring 2001. Proceedings, Rhodes, Greece, 6–9 May 2001; Volume 1, pp. 464–468. [Google Scholar]
- Cheung, K.W.; Sau, J.M.; Murch, R. A new empirical model for indoor propagation prediction. IEEE Trans. Veh. Technol.
**1998**, 47, 996–1001. [Google Scholar] [CrossRef] - Mao, G.; Anderson, B.D.; Fidan, B. Path loss exponent estimation for wireless sensor network localization. Comput. Netw.
**2007**, 51, 2467–2483. [Google Scholar] [CrossRef] - Mazuelas, S.; Lago, F.A.; González, D.; Bahillo, A.; Blas, J.; Fernandez, P.; Lorenzo, R.M.; Abril, E.J. Dynamic estimation of optimum path loss model in a RSS positioning system. In Proceedings of the 2008 IEEE/ION Position, Location and Navigation Symposium, Monterey, CA, USA, 5–8 May 2008; pp. 679–684. [Google Scholar]
- Rodas, J.; Escudero, C.J. Dynamic path-loss estimation using a particle filter. Int. J. Comput. Sci. Issues
**2010**, 7, 1. [Google Scholar] - Laoudias, C.; Moreira, A.; Kim, S.; Lee, S.; Wirola, L.; Fischione, C. A survey of enabling technologies for network localization, tracking, and navigation. IEEE Commun. Surv. Tutor.
**2018**, 20, 3607–3644. [Google Scholar] [CrossRef] - Carrera Villacrés, J.L.; Zhao, Z.; Braun, T. Discriminative Learning-based Smartphone Indoor Localization. arXiv
**2018**, arXiv:1804.03961. [Google Scholar] - Hamida, E.B.; Chelius, G. Investigating the impact of human activity on the performance of wireless networks—An experimental approach. In Proceedings of the 2010 IEEE International Symposium on “A World of Wireless Mobile and Multimedia Networks” (WoWMoM), Montrreal, QC, Canada, 14–17 June 2010; pp. 1–8. [Google Scholar]
- Perez-Vega, C.; Garcia, J.L.G. A simple approach to a statistical path loss model for indoor communications. In Proceedings of the 1997 27th European Microwave Conference, Jerusalem, Israel, 8–12 September 1997; Volume 1, pp. 617–623. [Google Scholar]
- Umer, M.; Kulik, L.; Tanin, E. Spatial interpolation in wireless sensor networks: Localized algorithms for variogram modeling and Kriging. Geoinformatica
**2010**, 14, 101–134. [Google Scholar] [CrossRef] - Ku, W. Application of Regression Kriging on the Spatial Prediction of Total Soil Nitrogen. Chin. Agric. Sci. Bull.
**2013**, 20, 029. [Google Scholar] - Mazuelas, S.; Bahillo, A.; Lorenzo, R.; Fernandez, P.; Lago, F.; Garcia, E.; Blas, J.; Abril, E. Robust Indoor Positioning Provided by Real-Time RSSI Values in Unmodified WLAN Networks. J. Sel. Top. Signal Process.
**2009**, 3, 821–831. [Google Scholar] [CrossRef] - Cisco Systems. Wi-Fi Location-Based Services 4.1 Design Guide. 2008. Available online: https://www.cisco.com/c/en/us/td/docs/solutions/Enterprise/Mobility/WiFiLBS-DG.pdf (accessed on 20 March 2019).

**Figure 10.**Trajectories of in-motion RSSI captures: for calibration (

**left**) and for tracking (

**right**).

**Figure 11.**Tracking accuracy per signal propagation representation: mean error (

**left**) and cummulative distribution function (

**right**).

**Table 1.**Path loss exponent values of Static One (SO) and in-Motion One (MO) representations of all Access Points (AP).

AP1 | AP2 | AP3 | AP4 | AP5 | |||||
---|---|---|---|---|---|---|---|---|---|

SO | MO | SO | MO | SO | MO | SO | MO | SO | MO |

3.458 | 2.599 | 3.212 | 2.423 | 3.092 | 2.029 | 3.234 | 2.571 | 3.287 | 2.712 |

**Table 2.**Path loss exponent values of Static Zone (SZ) and in-Motion Zone (MZ) representations of all Access Points (AP). Bold-marked values correspond to LOS situations.

AP1 | AP2 | AP3 | AP4 | AP5 | ||||||
---|---|---|---|---|---|---|---|---|---|---|

Zone | SZ | MZ | SZ | MZ | SZ | MZ | SZ | MZ | SZ | MZ |

A | 3.234 | 2.269 | 2.882 | 2.033 | 3.154 | 2.303 | 3.344 | 2.867 | 3.665 | 3.067 |

B | 3.232 | 2.639 | 2.794 | 2.447 | 2.908 | 2.026 | 1.739 | 2.555 | 3.175 | 2.785 |

C | 3.638 | 2.939 | 3.510 | 2.725 | 3.119 | 1.902 | 2.743 | 2.203 | 3.161 | 2.209 |

D | 2.511 | 2.554 | 2.519 | 2.538 | 2.048 | 1.808 | 2.989 | 2.692 | 3.126 | 2.871 |

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Martínez del Horno, M.; García-Varea, I.; Orozco Barbosa, L.
Calibration of Wi-Fi-Based Indoor Tracking Systems for Android-Based Smartphones. *Remote Sens.* **2019**, *11*, 1072.
https://doi.org/10.3390/rs11091072

**AMA Style**

Martínez del Horno M, García-Varea I, Orozco Barbosa L.
Calibration of Wi-Fi-Based Indoor Tracking Systems for Android-Based Smartphones. *Remote Sensing*. 2019; 11(9):1072.
https://doi.org/10.3390/rs11091072

**Chicago/Turabian Style**

Martínez del Horno, Miguel, Ismael García-Varea, and Luis Orozco Barbosa.
2019. "Calibration of Wi-Fi-Based Indoor Tracking Systems for Android-Based Smartphones" *Remote Sensing* 11, no. 9: 1072.
https://doi.org/10.3390/rs11091072