# Hybrid Urban Canyon Pedestrian Navigation Scheme Combined PDR, GNSS and Beacon Based on Smartphone

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## Abstract

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## 1. Introduction

- (1)
- Smart phone heading is vital to pedestrian navigation. In this study, we process and compare multiple heading methods and utilise their advantages to construct a comprehensive heading fusion strategy that combines the measurements of smart phone internal sensors’ and external beacon.
- (2)
- Most researchers have used beacon for positioning due to its high precision and easy installation characteristics. In this study, we develop related algorithms and workflow in using beacon RSSI to construct ground truth and calibrate smart phone heading continuously, in this manner, IMU cumulative error and impact of environment disturbance can be reduced, which improves positioning accuracy.
- (3)
- We research multiple fusion positioning algorithms, such as UKF, EKF and PF, to integrate GNSS, inertial navigation and beacon positioning results. Then, we design a noise evaluation algorithm and fusion strategy to construct a comprehensive solution. Tests verify that the algorithm works well in a consumption-level positioning device, such as a smart phone.
- (4)
- In this study, we perform tests in Hong Kong’s streets, which are a typical urban canyon. Hong Kong is a crowded city with many pedestrians and vehicles, high-density tall buildings and narrow streets. At present, the positioning capability of most consumption-level navigation devices in Hong Kong is poor, often producing large errors. Thus, tests and research to improve positioning capability in such a harsh environment is important.

## 2. Methodology

#### 2.1. PDR Algorithm

#### 2.2. Coordinate System and Initial Position for Localization

#### 2.3. Step Detection

#### 2.4. Step Length

#### 2.5. Heading Computation Using MEMS Sensors’ Measurements

#### 2.6. Improved Heading Algorithm Combining Accelerometer, Gyroscope, Magnetometer Measurements

#### 2.7. Beacon Positioning and Heading Computation

#### 2.8. Heading Fusion Combining Improved MEMS Heading and Beacon Heading

Algorithm 1: Heading fusion algorithm |

#### 2.9. GNSS/DGNSS Positioning and Noise Evaluation

Algorithm 2: GNSS measurement noise evaluation algorithm |

#### 2.10. Integration of PDR, Beacon and GNSS

## 3. Evaluation and Experiment

#### 3.1. Navigation Application Development

- (1)
- MEMS sensor measurement processing. In this part, the program reads measurements from smart phone internal sensors (particularly, accelerometer, gyroscope, magnetometer, barometer and thermometer) using Android APIs. Low-pass filtering algorithm is used to process raw data. These data are used to detect step movement, estimate step length and calculate smart phone attitude.
- (2)
- GNSS observation synchronization and processing. Smart phone internal location can be obtained Android APIs, calculated by GNSS and surround telecommunication base stations. However, smart phones are not a professional geodetic GNSS receiver. Smart phones can track low SNR and has high sensitivity; moreover, in a harsh environment, NLOS and multipath degrade smart phones’ positioning capability and stability severely. PDR can only compute the relative position, whereas GNSS can provide global WGS84 coordinates, that is, the absolute positioning. In our solution, we want to combine PDR andGNSS; thus, improving GNSS position accuracy is vital. On the basis of the requirements, a smart phone application sends a pseudorange, a carrier phase and NMEA to our DGPS server to develop a related algorithm for improving GNSS positioning accuracy and then send it back to the smart phone. Many smart phones do not output GNSS raw measurements, except for Huawei P20 and Mate20.
- (3)
- Beacon scanning and processing algorithms. In this part, we focus on beacon scanning, construction of beacon heading, step length calibration and beacon positioning. Beacon scanning is always running in the background of smart phones; thus, it can scan surrounding beacons continuously nearly in real-time. Once a new signal is tracked, the RSSI series is used to construct heading. Different heading construction algorithms are designed for the weak, middle and strong beacons. In addition to the heading, the weak beacon is used to calibrate the step length and PDR position.
- (4)
- Positioning fusion algorithm and mapping. Positioning fusion is the key issue of our solution. Here, we design an EKF model to integrate GNSS/DGNSS results, beacon location and PDR. Once the filtered position is obtained, the location and walking trajectory can be updated and displayed in Google Maps in real time.

#### 3.2. Smart Phone Heading Test

#### 3.3. Navigation Test Using GNSS + PDR + Beacon Fusion Scheme

#### 3.4. Navigation Test without Beacon

## 4. Discussion

#### 4.1. Discussion of Heading Test

#### 4.2. Discussion of Navigation Test

## 5. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## Abbreviations

LOT | Location of Things |

GNSS | Global Navigation Satellite System |

DGPS | Differential Global Positioning System |

MEMS | Micro-electromechanical System Sensor |

PDR | Pedestrian Dead Reckon |

EKF | Extended Kalman Filter |

PDOP | Position Dilution of Precision |

VDOP | Vertical Dilution of Precision |

HDOP | Horizontal Dilution of Precision |

FFT | Fast Fourier Transform |

RSSI | Radio Signal Strength Indication |

3D | Three-dimensional |

LOS | Line-of-sight |

NLOS | Non-line-of-sight |

IGRF | International Geomagnetic Reference Field |

NMEA | National Marine Electronics Association |

SNR | Signal to Noise Ratio |

UWB | Ultra Wideband |

UKF | Unscented Kalman Filter |

INS | Inertial Navigation System |

PF | Particle Filter |

API | Application Programming Interface |

WGS84 | World Geodetic System 1984 |

STD | Standard Deviation |

RMSE | Root Mean Squared Error |

RTK | Real-time Kinematic |

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**Figure 1.**Three-axis accelerometer measurements. The black and green lines denote the accelerometer measurement time series in the X- and Y-directions, respectively; and the red line represents the accelerometer measurement in the Z-axis. The blue line indicates the comprehensive time series of the three axes, which is calculated using Formula (2).

**Figure 2.**Step length adjustment strategy using a weak beacon. The pedestrian walks from weak beacon A (step number is W) to B (step number is M) and C (step number is N). From A to B, the pedestrian walks along the black line; from B to C, the pedestrian walks along the red line.

**Figure 3.**Beacon (strong, middle and weak) installation strategy and signal transmitting ranges. The three types of beacon are installed on each site.

**Figure 4.**Received RSSI change situation. The blue, red and green lines denote the RSSI of the weak, strong and middle beacons, respectively. The signal is received by a smart phone and a pedestrian goes across the three sites. Each site is installed with one weak, one middle and one strong beacon.

**Figure 6.**Left picture shows we measure the control point by RTK, the central picture presents the smart phone static test, the right picture shows the tester holds the smart phone in kinematic test.

**Figure 7.**The four graphs show test results along the straight line 1−2; the cyan line denotes raw smart phone heading computed from accelerometer and magnetometer measurements; green, red and blue lines represent the smart phone heading processed by Kalman filter, first-order complementary filter, improved MEMS algorithm separately. (

**a**) is Huawei Mate20 static test result, (

**b**) is kinematic result. (

**c**,

**d**) are static and kinematic results of Huawei AL10 respectively.

**Figure 8.**The four graphs show test results along the straight line 3–2; the cyan line denotes raw smart phone heading computed from accelerometer and magnetometer measurements; green, red and blue lines represent the smart phone heading processed by Kalman filter, First-order Complementary, improved MEMS algorithm separately. (

**a**) is Huawei Mate20 static test result, (

**b**) is kinematic result. (

**c**,

**d**) are static and kinematic results of Huawei AL10 respectively.

**Figure 9.**(

**a**) Test location and field surrounding environment. (

**b**) Narrow test street with both sides filled with tall buildings. (

**c**) Group of beacons installed on a lamp post. (

**d**) Graph showing the tester hold the test phone P20.

**Figure 10.**Lamp post locations (red circles) where the beacons are installed, tester walking route (blue line) and beacon heading (green line). The numbers, such as ‘1339’, denote the code of the lamp post for distinguishing the group code of beacons.

**Figure 11.**(

**a**) GNSS precision of smart phone reported by Android API. (

**b**) Satellite number obtained from NMEA. (

**c**) DOP information obtained from NMEA messages (blue, red and cyan denote PDOP, HDOP and VDOP, respectively).

**Figure 12.**The green, blue, gray and red lines represent the smart phone outputted GNSS position, the fused navigation position, the actual tester walking route and the connection of all installed beacon positions, respectively. The blue marker expresses the real-time navigation position using the hybrid positioning strategy.

**Figure 14.**Smart phone internal GNSS observation information obtained from NMEA messages. The first graph shows the SNR values of satellites, the second graph shows the tracked satellite number of the smart phone and the third graph shows the HDOP series.

**Figure 15.**Field navigation test results in (

**a**) Waichai and (

**b**) Mong Kok. The black, blue, red and green lines represent the actual pedestrian walking route, the fused navigation position, pure PDR positions and the trajectory obtained from the smart phone internal GNSS positions, respectively. The blue marker denotes the real-time navigation location of the tester using the fused navigation algorithm.

**Figure 16.**Cumulative distribution function (CDF) graph of the positioning error in (

**a**) Portland and Shanghai Streets and (

**b**) Argyle and Shandong Streets. The red and blue lines denote the smart phone internal GNSS and fused position, respectively.

**Figure 17.**CDF graph of the positioning error. The green, red and blue lines denote the PDR result, the smart phone internal GNSS result and the fused strategy positioning result, respectively.

Formula | Parameters | Reference |
---|---|---|

$SL=k*H$ | H is the height and k is equals to 0.415 for male and 0.413. | Pratama |

$SL=k*\sqrt[4]{ac{c}_{max}-ac{c}_{min}}$ | $ac{c}_{max}$ and $ac{c}_{min}$ represents the maximum and minimum vertical acceleration values in a single stride, k is a constant model parameter. | Weinberg |

$SL=k*H*\sqrt{SF}$ | H and $SF$ represents the height of the subject and step frequency and k is a model parameter that is tuned to 0.3139 for male and 0.2975 for female. | Tian |

$SL=k*\sqrt[3]{\frac{{\sum}_{k=1}^{N}\left|ac{c}_{k}\right|}{N}}$ | $ac{c}_{k}$ is the acceleration measured on a sample in a single step and N is the number of samples corresponding to each step. | Kim |

$SL=(0.7+a(H-1.75)+\frac{b(SF-1.79)H}{1.75})c$ | $SL$ and $SF$ represents the step length and step frequency H is the height of the pedestrian which is manually inserted in this step model, a, b and c are model parameters for each person and can be calibrated by pre-training. | Ruizhi Chen |

$SL=h*(a*{f}_{step}+b)+c$ | h and ${f}_{step}$ represents the pedestrian height and step frequency a, b and c are pre-trained parameters. | Valerie Renaudin |

**Table 2.**Related information of the hardware and software, Android smart phone and beacon application for the Mong Kok test.

Application | Beacon | Smart Phone (Huawei P20) |
---|---|---|

Map: | Communication way: BLE 4.0 | Sensors: |

Google Maps | Broadcast power: −30~−40 dBm | Gravity sensor |

Operating system: | Ambient light sensor | Proximity sensor |

Android | Broadcast Frequency: 100~10,000 ms | Front fingerprint sensor |

Installed version: | Transmitting distance: 3~100 m | Hall sensor |

>Android OS 4.3 | Supporting OS: | Gyroscope |

Above iOS 7.0 and Android 4.3 | Compass | |

Brand: Zhishi | Color temperature sensor | |

GNSS: GPS, GLONASS and Beidou | ||

Chip: | ||

Huawei Kirin 970 CPU octa core | ||

4 × Cortex A73 2.36 GHz | ||

4 × Cortex A53 1.8 GHz | ||

Memory: | ||

GB RAM + 128 GB ROM | ||

Operating system: Android™ 8.1 |

Start Lamp Post ID | End Lamp Post ID | Azimuth (deg) |
---|---|---|

1339 | 1340 | 168.707 |

1340 | 1339 | 348.707 |

1340 | 1341 | 168.707 |

1341 | 1340 | 348.707 |

1341 | 1342 | 168.707 |

1342 | 1341 | 348.707 |

1342 | 1343 | 168.707 |

1343 | 1342 | 348.707 |

1343 | 1344 | 168.707 |

1344 | 1343 | 348.707 |

1350 | 1351 | 169.311 |

1351 | 1350 | 349.311 |

1351 | 1352 | 169.311 |

1352 | 1351 | 349.311 |

1353 | 1354 | 169.311 |

1354 | 1353 | 349.311 |

1350 | 1349 | 78.835 |

1349 | 1350 | 258.853 |

Test_Phone | Algorithm | 3–2 (−11.5) | 1–2 (87.97) | ||||
---|---|---|---|---|---|---|---|

Mean Bias | RMSE | STD | Mean Bias | RMSE | STD | ||

AL10 | Improved | 6.11 | 6.14 | 0.59 | 3.16 | 3.19 | 0.46 |

MagAccOrientation | 5.69 | 5.75 | 0.81 | 2.77 | 2.90 | 0.86 | |

First-Order CF | 5.70 | 5.75 | 0.72 | 2.81 | 3.02 | 1.12 | |

Kalman filter | 5.69 | 5.73 | 0.70 | 2.73 | 3.24 | 1.74 | |

Mate20 | Improved | 3.26 | 3.34 | 0.73 | −2.27 | 2.28 | 0.25 |

MagAccOrientation | 3.26 | 3.39 | 0.91 | −2.27 | 2.32 | 0.50 | |

First-Order CF | 3.26 | 3.36 | 0.80 | −2.29 | 2.44 | 0.85 | |

Kalman filter | 3.27 | 3.36 | 0.79 | −2.31 | 2.77 | 1.52 |

Test_Phone | Algorithm | 3–2 (−11.5) | 1–2 (87.97) | ||||
---|---|---|---|---|---|---|---|

Mean Bias | RMSE | STD | Mean Bias | RMSE | STD | ||

AL10 | Improved | 5.97 | 6.82 | 3.48 | 4.72 | 5.65 | 3.11 |

MagAccOrientation | 5.98 | 7 | 3.81 | 4.23 | 6.71 | 5.21 | |

First-Order CF | 5.99 | 6.57 | 2.93 | 4.24 | 5.53 | 3.56 | |

Kalman filter | 6 | 6.49 | 2.73 | 4.19 | 5.4 | 3.41 | |

Mate20 | Improved | 1.18 | 3.56 | 3.41 | −1.5 | 3.11 | 2.72 |

MagAccOrientation | 1.17 | 4.77 | 4.67 | −1.44 | 3.96 | 3.68 | |

First-Order CF | 1.18 | 3.39 | 3.24 | −1.46 | 3.2 | 2.85 | |

Kalman filter | 1.18 | 3.16 | 3 | −1.48 | 3.22 | 2.86 |

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## Share and Cite

**MDPI and ACS Style**

Ye, J.; Li, Y.; Luo, H.; Wang, J.; Chen, W.; Zhang, Q.
Hybrid Urban Canyon Pedestrian Navigation Scheme Combined PDR, GNSS and Beacon Based on Smartphone. *Remote Sens.* **2019**, *11*, 2174.
https://doi.org/10.3390/rs11182174

**AMA Style**

Ye J, Li Y, Luo H, Wang J, Chen W, Zhang Q.
Hybrid Urban Canyon Pedestrian Navigation Scheme Combined PDR, GNSS and Beacon Based on Smartphone. *Remote Sensing*. 2019; 11(18):2174.
https://doi.org/10.3390/rs11182174

**Chicago/Turabian Style**

Ye, Junhua, Yaxin Li, Huan Luo, Jingxian Wang, Wu Chen, and Qin Zhang.
2019. "Hybrid Urban Canyon Pedestrian Navigation Scheme Combined PDR, GNSS and Beacon Based on Smartphone" *Remote Sensing* 11, no. 18: 2174.
https://doi.org/10.3390/rs11182174