# An Improved Pedestrian Navigation Method Based on the Combination of Indoor Map Assistance and Adaptive Particle Filter

^{1}

^{2}

^{3}

^{*}

## Abstract

**:**

^{2}, the average error and the maximum error of the position are less than two meters relative to the reference point.

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Proposed System Scheme

#### 2.2. Pedestrian Navigation Method Based on Indoor MA and PF

#### 2.2.1. Theoretical Model of Algorithm

- (1)
- Pedestrian navigation model based on dead reckoning

- (2)
- Observation model based on particle “not going through the wall” method

#### 2.2.2. Algorithm Process Design

- (1)
- Optimization of initial position and heading of particle set

- (2)
- One-step prediction of particle states

- (3)
- Particle weight updating based on prior map information

- (4)
- Particle resampling based on adaptive particle number

#### 2.2.3. Algorithm Flow

## 3. Results

#### 3.1. Verification of Simulation

#### 3.1.1. Conditions of Simulation

`→`②

`→`③

`→`④

`→`① has a distance of 207.72 m. The simulation moves two circles in a counterclockwise direction. The parameters are set as follows: The mean square error of the step noise is 0.1 m, and the mean square error of heading change noise is 1°. The simulation data of pedestrian position and course change obtained are also saved. The four reference coordinates in the trace are shown in Figure 6b. Due to the process of entering the room, the total distance cannot be measured accurately, and the total distance exceeds 415.44 m.

#### 3.1.2. Analysis of Simulation Results

- (1)
- The initial position and heading of pedestrian are known

- (2)
- The initial position and heading of pedestrian are unknown (adaptive particle number)

- (3)
- The initial position and heading of pedestrian are unknown (fixed particle number)

#### 3.2. Experiment and Verification

#### 3.2.1. Experimental Conditions

#### 3.2.2. Experimental Verification Analysis

- (1)
- MCIN method

- (2)
- The initial position and heading of pedestrian are known

- (3)
- The initial position and heading of pedestrian are unknown (adaptive particle number)

- (4)
- The initial position and heading of pedestrian are unknown (fixed particle number)

## 4. Discussion

^{2}, when the total distance exceeds 415.44 m, the mean error and the maximum error of the position relative to the reference point are both less than 2 m. It effectively suppresses the pedestrian navigation error based on inertial devices, and greatly improves the calculation efficiency, which can meet the needs of indoor pedestrians for a long time.

## 5. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## References

- Jwo, D.J.; Lee, J.T. Kernel Entropy Based Extended Kalman Filter for GPS Navigation Processing. Comput. Mater. Contin.
**2021**, 68, 857–876. [Google Scholar] - Cao, X.; Shen, F.; Zhang, S.; Li, J. Time delay bias between the second and third generation of BeiDou Navigation Satellite System and its effect on precise point positioning. Measurement
**2021**, 168, 108346. [Google Scholar] [CrossRef] - Kröll, H.; Steiner, C. Indoor ultra-wideband location fingerprinting. In Proceedings of the 2010 International Conference on Indoor Positioning and Indoor Navigation, Zurich, Switzerland, 15–17 September 2010. [Google Scholar]
- Li, Y.; Zhuang, Y.; Zhang, P.; Lan, H.; Niu, X.; Naser, E.-S. An improved inertial/wifi/magnetic fusion structure for indoor navigation. Inf. Fusion
**2017**, 34, 101–119. [Google Scholar] - Kriz, P.; Maly, F.; Kozel, T. Improving Indoor Localization Using Bluetooth Low Energy Beacons. Mob. Inf. Syst.
**2016**, 2016 Pt 2, 1–11. [Google Scholar] [CrossRef] [Green Version] - Bianchi, V.; Ciampolini, P.; De Munari, I. RSSI-Based Indoor Localization and Identification for ZigBee Wireless Sensor Networks in Smart Homes. IEEE Trans. Instrum. Meas.
**2019**, 68, 566–575. [Google Scholar] [CrossRef] - Chawla, K.; Thomason, W.; Mcfarland, C.; Robins, G. An accurate real-time RFID-based location system. Int. J. Radio Freq. Identif. Technol. Appl.
**2018**, 5, 48–76. [Google Scholar] [CrossRef] - Nadarajah, V.R.; Singh, M.M. Privacy-by-Design(PbD) IoT Framework: A Case of Location Privacy Mitigation Strategies for Near Field Communication (NFC) Tag Sensor. Adv. Sci. Technol. Eng. Syst. J.
**2017**, 2, 134–138. [Google Scholar] [CrossRef] [Green Version] - Tian, D.; Ming, D.; Mao, G.; Lin, Z.; David, L.P. Uplink Performance Analysis of Dense Cellular Networks with LoS and NLoS Transmissions. IEEE Trans. Wirel. Commun.
**2017**, 16, 2601–2613. [Google Scholar] - Mao, K.J.; Wu, J.B.; Jin, H.B.; Miao, C.Y.; Chen, Q.Z. Indoor localization algorithm for NLOS environment. Acta Electron. Sin.
**2016**, 44, 1174–1179. [Google Scholar] - Marek, P.; Philippe, G.; Rasmus, A. Mapping forests using an unmanned ground vehicle with 3D LiDAR and graph-SLAM. Comput. Electron. Agric.
**2018**, 145, 217–225. [Google Scholar] - Liu, J.; Gao, W.; Hu, Z. Bidirectional Trajectory Computation for Odometer-Aided Visual-Inertial SLAM. IEEE Robot. Autom. Lett.
**2021**, 99, 1670–1677. [Google Scholar] [CrossRef] - Qureshi, U.; Golnaraghi, F. An algorithm for the in-field calibration of a MEMS IMU. IEEE Sens. J.
**2017**, 17, 7479–7486. [Google Scholar] - Ahmed, D.B.; Diaz, E.M. Loose Coupling of Wearable-Based INSs with Automatic Heading Evaluation. Sensors
**2017**, 17, 2534. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Xu, L.; Xiong, Z.; Liu, J.; Wang, Z.; Ding, Y. A Novel Pedestrian Dead Reckoning Algorithm for Multi-Mode Recognition Based on Smartphones. Remote Sens.
**2019**, 11, 294. [Google Scholar] [CrossRef] [Green Version] - Ding, Y.; Xiong, Z.; Li, W.; Cao, Z.; Wang, Z. Pedestrian Navigation System with Trinal-IMUs for Drastic Motions. Sensors
**2020**, 20, 5570. [Google Scholar] - Ruppelt, J.; Kronenwett, N.; Scholz, G.; Trommer, G.F. High-precision and robust indoor localization based on foot-mounted inertial sensors. In Proceedings of the 2016 IEEE/ION Position, Location and Navigation Symposium (PLANS), Savannah, GA, USA, 11–14 April 2016; pp. 67–75. [Google Scholar]
- Hsu, Y.L.; Wang, J.S.; Chang, C.W. A Wearable Inertial Pedestrian Navigation System with Quaternion-Based Extended Kalman Filter for Pedestrian Localization. IEEE Sens. J.
**2017**, 17, 3193–3206. [Google Scholar] [CrossRef] - Suresh, R.P.; Sridhar, V.; Pramod, J.; Talasila, V. Zero velocity potential update (ZUPT) as a correction technique. In Proceedings of the 2018 3rd International Conference on Internet of Things: Smart Innovation and Usages (IoT-SIU), Bhimtal, India, 23–24 February 2018; pp. 1–8. [Google Scholar]
- Abdallah, A.A.; Jao, C.S.; Kassas, Z.M.; Shkel, A.M. A pedestrian indoor navigation system using deep-learning-aided cellular signals and ZUPT-aided foot-mounted IMUs. IEEE Sens. J.
**2021**, 22, 5188–5198. [Google Scholar] [CrossRef] - Ilyas, M.; Cho, K.; Baeg, S.-H.; Park, S. Drift reduction in pedestrian navigation system by exploiting motion constraints and magnetic field. Sensors
**2016**, 16, 1455. [Google Scholar] [CrossRef] [Green Version] - Song, J.W.; Park, C.G. Enhanced pedestrian navigation based on course angle error estimation using cascaded Kalman filters. Sensors
**2018**, 18, 1281. [Google Scholar] [CrossRef] [Green Version] - Diez, L.E.; Bahillo, A.; Bataineh, S.; Masegosa, A.D.; Perallos, A. Enhancing improved heuristic drift elimination for wrist-worn PDR systems in buildings. In Proceedings of the 2016 IEEE 84th Vehicular Technology Conference (VTC-Fall), Montreal, QC, Canada, 18–21 September 2016; pp. 1–5. [Google Scholar]
- Muhammad, M.N.; Salcic, Z.; Wang, K.I.-K. Detecting turns and correcting headings using low-cost INS. J. Navig.
**2018**, 71, 189–207. [Google Scholar] [CrossRef] - Kim, S.-Y.; Park, Y.-Y.; Lee, K.-O.; Jung, J.-B.; Kye, J.-E. A Study on the Making Indoor Topology Map for the Autonomous Driving of Wearable Robots. Ilkogr. Online
**2021**, 20, 1153–1159. [Google Scholar] - Angermann, M.; Robertson, P. FootSLAM: Pedestrian Simultaneous Localization and Mapping Without Exteroceptive Sensors—Hitchhiking on human perception and cognition. Proc. IEEE
**2012**, 100, 1840–1848. [Google Scholar] [CrossRef] - Kaiser, S.; Puyol, M.G.; Robertson, P. Measuring the Uncertainty of Probabilistic Maps Representing Human Motion for Indoor Navigation. Mob. Inf. Syst.
**2016**, 2016, 9595306. [Google Scholar] [CrossRef] - Wang, Q.; Luo, H.; Men, A.; Zhao, F.; Huang, Y. An Infrastructure-Free Indoor Localization Algorithm for Smartphones. Sensors
**2018**, 18, 3317. [Google Scholar] [CrossRef] [Green Version] - Yu, C.; El-Sheimy, N.; Lan, H.; Liu, Z. Map-based indoor pedestrian navigation using an auxiliary particle filter. Micromachines
**2017**, 8, 225. [Google Scholar] [CrossRef] [Green Version] - Wang, Z.; Xiong, Z.; Xing, L.; Ding, Y.; Sun, Y. A method for autonomous multi-motion modes recognition and navigation optimization for indoor pedestrian. Sensors
**2022**, 22, 5022. [Google Scholar] [CrossRef]

**Figure 1.**General scheme of the indoor pedestrian navigation and location method based on the IMAPF method.

**Figure 6.**Simulation trace and coordinates of reference point. (

**a**) Simulation trace. (

**b**) Schematic diagram of the coordinates of the reference point.

**Figure 7.**Sampling particle distribution and motion trace with known position and heading. (

**a**) Particle distribution at initial moment. (

**b**) Particle distribution in motion.

**Figure 8.**Navigation trace comparison and positioning error CDF curve with known initial position and heading. (

**a**) Comparison diagram of positioning trace. (

**b**) CDF curve of positioning error. (

**c**) Absolute position error of each step.

**Figure 9.**Sampling particle distribution and motion trace with unknown initial position and heading. (

**a**) Particle distribution at initial moment; (

**b**) particle distribution and motion trace of the 44th step in the motion process; (

**c**) particle distribution and motion trace of the 74th step in the motion process; and (

**d**) particle distribution and motion trace of the 162th step in the motion process.

**Figure 10.**Navigation trace comparison and positioning error CDF curve with unknown initial position and heading. (

**a**) Comparison diagram of positioning trace; (

**b**) CDF curve of positioning error; and (

**c**) absolute position error of each step.

**Figure 11.**Pedestrian motion trace calculated with different particle numbers under simulation conditions.

**Figure 12.**Schematic diagram of sensor installation. (

**a**) Installation diagram of sensors on waist. (

**b**) Installation diagram of sensors on leg and foot.

**Figure 13.**Indoor experiment scene and experiment trace. (

**a**) Indoor experiment scene. (

**b**) Reference points.

**Figure 14.**Two dimensional trace and absolute value of positioning error based on MCIN method. (

**a**) Two dimensional trace. (

**b**) Absolute value of positioning error.

**Figure 15.**Sampling particle distribution and motion trace under the condition of known initial position and heading. (

**a**) Particle distribution at the initial moment. (

**b**) Particle distribution and trace during motion.

**Figure 16.**Navigation trace comparison and curve of absolute value of positioning error with known initial position and heading. (

**a**) Positioning trace comparison diagram. (

**b**) Absolute value of positioning error.

**Figure 17.**Distribution of sampled particles and motion trace with unknown initial position and heading. (

**a**) Particle distribution at initial moment. (

**b**) Particle distribution and motion track of the 58th step in the motion process. (

**c**) Particle distribution and motion track of the 114th step in the motion process. (

**d**) Particle distribution and motion track of the 190th step in the motion process.

**Figure 18.**Navigation trace comparison and curve of absolute value of positioning error with unknown initial position and heading. (

**a**) Positioning trace comparison diagram. (

**b**) Absolute value of the positioning error.

**Figure 19.**Pedestrian motion trace calculated with different particle numbers under experimental conditions.

Characteristic | Parameter |
---|---|

Computer operating system | Windows10 |

CPU | Intel(R) Core(TM) i7-8700, Dominant frequency 3.20 GHz |

Memory | 32 GB |

Software | Matlab2020 |

Navigation Method | Mean Error (m) | Maximum Error (m) |
---|---|---|

PDR | 4.78 | 11.81 |

IMAPF | 0.44 | 1.18 |

**Table 3.**Statistics of positioning errors with different particle numbers under simulation conditions.

Particle Number | Mean Error (m) | Maximum Error (m) | Calculation Time (s) |
---|---|---|---|

2 thousand fixed particles | 10.34 | 15.99 | 16.64 |

10 thousand fixed particles | 7.74 | 12.07 | 103.05 |

50 thousand fixed particles | 0.75 | 3.01 | 909.04 |

100 thousand fixed particles | 0.50 | 1.91 | 2624.36 |

adaptive particle numbers | 0.36 | 0.84 | 116.14 |

/ | Sensor Range | Bias Stability |
---|---|---|

Accelerometer | $\pm 6\text{}\mathrm{g}$ | $10\text{}\mathsf{\mu}\mathrm{g}$ |

Gyroscope | $\pm 500\text{}\mathrm{deg}/\mathrm{s}$ | $1.0\text{}\mathrm{deg}/\mathrm{h}$ |

/ | Sensor Range | Total Root Mean Square Noise |
---|---|---|

Barometer | $300\u20131100\text{}\mathrm{mBar}$ | $3.6\text{}\mathrm{Pa}$ |

Navigation Method | Mean Error (m) | Maximum Error (m) |
---|---|---|

MCIN | 1.98 | 4.16 |

IMAPF | 0.54 | 0.98 |

**Table 7.**Statistics of positioning errors with different particle numbers under experimental conditions.

Particle number | Mean Error (m) | Maximum Error (m) | Calculation Time (s) |
---|---|---|---|

2 thousand fixed particles | 3.89 | 7.03 | 16.37 |

10 thousand fixed particles | 2.74 | 9.21 | 93.02 |

50 thousand fixed particles | 1.13 | 1.40 | 755.33 |

100 thousand fixed particles | 1.04 | 1.11 | 2229.13 |

adaptive particle number | 1.06 | 1.33 | 131.59 |

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |

© 2022 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 (https://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Wang, Z.; Xing, L.; Xiong, Z.; Ding, Y.; Sun, Y.; Shi, C.
An Improved Pedestrian Navigation Method Based on the Combination of Indoor Map Assistance and Adaptive Particle Filter. *Remote Sens.* **2022**, *14*, 6282.
https://doi.org/10.3390/rs14246282

**AMA Style**

Wang Z, Xing L, Xiong Z, Ding Y, Sun Y, Shi C.
An Improved Pedestrian Navigation Method Based on the Combination of Indoor Map Assistance and Adaptive Particle Filter. *Remote Sensing*. 2022; 14(24):6282.
https://doi.org/10.3390/rs14246282

**Chicago/Turabian Style**

Wang, Zhengchun, Li Xing, Zhi Xiong, Yiming Ding, Yinshou Sun, and Chenfa Shi.
2022. "An Improved Pedestrian Navigation Method Based on the Combination of Indoor Map Assistance and Adaptive Particle Filter" *Remote Sensing* 14, no. 24: 6282.
https://doi.org/10.3390/rs14246282