# Autonomous Multi-Floor Localization Based on Smartphone-Integrated Sensors and Pedestrian Indoor Network

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## Abstract

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## 1. Introduction

- (1)
- This paper proposes a robust data and model dual-driven pedestrian trajectory estimator for accurate integrated sensor-based positioning in complex motion modes and disturbed environments. The proposed approach considers factors such as handheld modes, lateral errors, and step-length constraints while updating the location based on a period of observations rather than solely relying on the last moment;
- (2)
- A new floor detection algorithm based on Bi-LSTM is implemented to offer floor index references for estimated trajectories. This involves extracting hybrid features from wireless signals, human motion, and map-related data to improve the recognition precision, leading to a more accurate initial location and floor information provided to users;
- (3)
- This work models an extracted pedestrian indoor network, formulates it as the combination of a matrix, and develops a grid search algorithm for network matching and further walking route calibration with the reference of the initial location and floor detection results. The matched network information is further applied as the observation under the fusion phase;
- (4)
- Using the outcomes of trajectory estimation, floor recognition, and indoor network matching, an error ellipse-supported unscented Kalman filter (EE-UKF) is suggested to robustly combine data from integrated sensors, pedestrian motion, and indoor network information. This approach can achieve meter-level positioning accuracy without requiring additional local facilities for assistance.

## 2. Data and Model Dual-Driven Trajectory Estimator

#### 2.1. Hybrid Deep-Learning Model Enhanced Walking Speed Prediction

#### 2.2. Data and Model Dual-Driven Trajectory Estimator

## 3. Floor Detection, Network Matching, and Intelligent Fusion

#### 3.1. Bi-LSTM-Enhanced Floor Recognition

- (1)
- To capture the wireless features of the selected floor, the modeled RSSI values obtained from some representative Wi-Fi access points (APs) are utilized as a part of the input vector in the training procedure of the Bi-LSTM model. These collected RSSI values are deemed to be the most representative and are critical for ensuring accurate predictions:$$\left\{{\varphi}_{{}_{RSSI}}^{1}\begin{array}{ccc}{\varphi}_{{}_{RSSI}}^{2}& \cdots & {\varphi}_{{}_{RSSI}}^{k}\end{array}\right\}\ge T{h}_{RSSI}$$

- (2)
- The mean RSSI value of the selected representative RSSI values: In order to capture the overall description index of the RSSI collection, the mean RSSI value is also computed and included as an input value for the developed Bi-LSTM model. This additional input helps to provide a more comprehensive understanding of the RSSI vector, enabling the model to make more accurate predictions based on both individual signal strengths and the overall average signal strength:$${\varphi}_{{}_{RSSI}}^{Ave}={\displaystyle \sum _{i=1}^{k}{\varphi}_{{}_{RSSI}}^{i}}$$

- (3)
- Deviation between the collected representative RSSI values: The calculated deviation in the scanned RSSI vector can significantly capture the dynamic changes in the surrounding buildings and is therefore considered a crucial feature. By utilizing this feature, the proposed model can better adapt to variations in the environment and provide more accurate estimations as a result. Incorporating real-time differences between the scanned RSSI vectors enhances the model’s ability to detect subtle changes in the signal strength over time, allowing it to generate more reliable predictions:$${\varphi}_{{}_{RSSI}}^{Diff}=\left|{\varphi}_{{}_{RSSI}}^{k}-{\varphi}_{{}_{RSSI}}^{k-1}\right|$$
- (4)
- The norm vector of the extracted local magnetic data is calculated as follows:$${M}_{Norm}=\sqrt{{m}_{x}{}^{2}+{m}_{y}{}^{2}+{m}_{z}{}^{2}}$$
- (5)
- The barometric pressure increment values based on the initial data are calculated as follows:$$\Delta {\phi}_{Baro}^{i}={\phi}_{Baro}^{i}-{\phi}_{Baro}^{0}$$
- (6)
- The deviation between the adjacent collected pressure data is calculated as follows:$${\phi}_{{}_{Baro}}^{Diff}=\left|{\phi}_{{}_{{}_{Baro}}}^{k}-{\phi}_{{}_{{}_{Baro}}}^{k-1}\right|$$

#### 3.2. Indoor Network Matching and Trajectory Calibration

- (1)
- To detect turning points during pedestrian movement, a hybrid deep-learning framework is utilized to detect changeable handheld modes and determine the walking direction. This approach is particularly useful in complex environments where pedestrians may adopt various postures or holding positions. After identifying the forward direction, the turning points are calculated by peak recognition, similar to a step detection method proposed previously [1]. By incorporating these techniques, we can accurately estimate the user’s position and trajectory in indoor environments, facilitating effective navigation and positioning tasks. Moreover, this approach can be applied in diverse settings, such as healthcare, logistics, and security, enhancing the overall performance of indoor positioning systems;
- (2)
- To reduce the incidence of false matching, we exclusively considered the results provided by the trajectory estimator that contained more than three turning points, which can be further applied for network matching purposes. This algorithm was proven to significantly increase the precision of trajectory matching;
- (3)
- To effectively match trajectories with the existing indoor network, the proposed approach employs both the correlation coefficient value and the dynamic time warping (DTW) index. These two techniques are used in tandem to provide a more comprehensive understanding of the similarities and differences between different trajectories, enabling the model to identify optimal matches with greater accuracy. By incorporating both the correlation coefficient index and the DTW index, the proposed approach can enhance the overall performance of trajectory matching, paving the way for more precise location-based services within indoor environments. These indices enable us to identify similar trajectories by analyzing the detected turning points in each trajectory [14]. By employing this method, we can enhance the overall performance of indoor positioning systems, making them suitable for various applications such as logistics, security, and healthcare.$$\begin{array}{l}DTW({\beta}_{\tau -1},{\beta}_{\tau})\\ =Dist({p}_{j},{s}_{k})+\mathrm{min}[D({s}_{j-1},{p}_{k}),D({s}_{j},{p}_{k-1}),D({s}_{j-1},{p}_{k-1})]\end{array}$$$$\begin{array}{l}{\rho}_{cor}(x,y)={\rho}_{cor}({x}_{\tau -1},{x}_{\tau})+{\rho}_{cor}({y}_{\tau -1},{y}_{\tau})\\ =\frac{{{\displaystyle \sum}}_{i=1}^{M}\left({x}_{\tau -1}^{i}-\overline{{x}_{\tau -1}}\right)\left({x}_{\tau}^{i}-\overline{{x}_{\tau}}\right)}{\sqrt{{{\displaystyle \sum}}_{i=1}^{M}{\left({x}_{\tau -1}^{i}-\overline{{x}_{\tau -1}}\right)}^{2}}\sqrt{{{\displaystyle \sum}}_{i=1}^{2m+1}{\left({x}_{\tau}^{i}-\overline{{x}_{\tau}}\right)}^{2}}}\\ +\frac{{{\displaystyle \sum}}_{i=1}^{M}\left({y}_{\tau -1}^{i}-\overline{{y}_{\tau -1}}\right)\left({y}_{\tau}^{i}-\overline{{y}_{\tau}}\right)}{\sqrt{{{\displaystyle \sum}}_{i=1}^{M}{\left({y}_{\tau -1}^{i}-\overline{{y}_{\tau -1}}\right)}^{2}}\sqrt{{{\displaystyle \sum}}_{i=1}^{2m+1}{\left({y}_{\tau}^{i}-\overline{{y}_{\tau}}\right)}^{2}}}\end{array}$$
- (4)
- Following the map-matching phase, the matched turning points on the extracted pedestrian network are utilized as absolute references for the trajectory calibration phase of straight lines. This approach enables us to accurately estimate the user’s position and trajectory, improving the overall performance of indoor positioning systems. By incorporating this method, we can facilitate various applications, such as asset tracking, navigation, and emergency response. Overall, this technique achieves a robust and practical approach for indoor localization tasks:$${\widehat{\mathit{x}}}_{k-1|k}={\widehat{\mathit{x}}}_{k-1}+{\mathit{P}}_{k-1}{\mathsf{\varphi}}_{k}^{T}{({\mathit{P}}_{{}_{k-1}}^{-})}^{-1}({\widehat{\mathit{x}}}_{k}-{\widehat{\mathit{x}}}_{k}{}^{-})$$$${\mathit{P}}_{k-1|k}={\mathit{P}}_{k-1}-({\mathit{P}}_{k-1}{\mathsf{\varphi}}_{k}^{T}{({\mathit{P}}_{k}{}^{-})}^{-1})({\mathit{P}}_{k}-{\mathit{P}}_{k}{}^{-})\cdot {({\mathit{P}}_{k-1}{\mathsf{\varphi}}_{k}^{T}{({\mathit{P}}_{k}{}^{-})}^{-1})}^{T}$$

#### 3.3. Error Ellipse-Enhanced UKF for Intelligent Fusion

_{1}, y

_{1}) can be utilized to compute the closest point of observation in the indoor network. Consequently, the resulting representation of the indoor network is defined as follows:

## 4. Experimental Results of ML-ISNM

#### 4.1. Performance Evaluation of Trajectory Estimator

#### 4.2. Performance Evaluation of Floor Recognition

#### 4.3. Precision Evaluation of Error Ellipse-Assisted UKF

## 5. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

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**Figure 9.**Multi-floor contained real-world indoor environment. (

**a**) 6th Floor. (

**b**) 7th Floor. (

**c**) 8th Floor. (

**d**) 9th Floor.

**Figure 11.**(

**a**). Two-dimensional trajectory comparison between TE and EE-UKF. (

**b**). Three-dimensional trajectory comparison between TE and EE-UKF.

Location Sources | Accuracy | Robustness | Complexity | Scalability | Cost |
---|---|---|---|---|---|

Wi-Fi [1] | Fingerprinting: 2~5 m; RTT ranging: 1~3 m | Affected by environmental factors and human bodies | Time-consuming for database generation | Easy | No additional cost |

Bluetooth/BLE [2] | Fingerprinting: 2~5 m AOA array: <1 m | Limited by the changeable environments | Time-consuming to construct the database | Easy | High cost of antenna array |

UWB [3] | Centimeter level | Good | Medium | Medium | High |

Sound Source [4] | Meter level | Affected by NLOS factor | Medium | Good | Medium |

Integrated Sensors [5] | Cumulative error exists | Good | Medium | Good | Low |

NFC [6] | Centimeter level, short effective distance | Good | Low | Easy | A large number of NFC tags are required |

Cellular Network [7] | Ten meters to tens of meters | Affected by environments | Medium | Good | High |

RFID [8] | 1~5 m | Affected by environments | Medium | Medium | Medium |

Infrared Ray [9] | Meter level | LOS required | Medium | Low | Medium, additional transceivers are required |

Visible Light [10] | 1~5 m | Medium | Medium | Good | Low |

Ultrasound [11] | Centimeter level | Good | Low | Low | Medium, additional transceivers are required |

Magnetic Field [12] | 2~5 m | Affected by environments | High | Good | Low |

Computer Vision [13] | Camera rendezvous: centimeter level; others: meter level | Medium, affected by the ambient light and quality of the image | Very high | Good | Medium |

Models | Maximum Error | 75th Percentile | Average Error |
---|---|---|---|

EE-UKF | 2.39 m | 1.35 m | 1.13 m |

MA-PF | 2.87 m | 1.62 m | 1.31 m |

HTrack | 3.42 m | 2.07 m | 1.68 m |

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## Share and Cite

**MDPI and ACS Style**

Shi, C.; Teng, W.; Zhang, Y.; Yu, Y.; Chen, L.; Chen, R.; Li, Q.
Autonomous Multi-Floor Localization Based on Smartphone-Integrated Sensors and Pedestrian Indoor Network. *Remote Sens.* **2023**, *15*, 2933.
https://doi.org/10.3390/rs15112933

**AMA Style**

Shi C, Teng W, Zhang Y, Yu Y, Chen L, Chen R, Li Q.
Autonomous Multi-Floor Localization Based on Smartphone-Integrated Sensors and Pedestrian Indoor Network. *Remote Sensing*. 2023; 15(11):2933.
https://doi.org/10.3390/rs15112933

**Chicago/Turabian Style**

Shi, Chaoyang, Wenxin Teng, Yi Zhang, Yue Yu, Liang Chen, Ruizhi Chen, and Qingquan Li.
2023. "Autonomous Multi-Floor Localization Based on Smartphone-Integrated Sensors and Pedestrian Indoor Network" *Remote Sensing* 15, no. 11: 2933.
https://doi.org/10.3390/rs15112933