# Fingerprint Positioning Method for Dual-Band Wi-Fi Based on Gaussian Process Regression and K-Nearest Neighbor

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## Abstract

**:**

## 1. Introduction

- (1)
- In this paper, we propose a dedicated dual-band fingerprint for dual-band Wi-Fi, which is generated by the normalized values of the RSS measurements of the 2.4 and 5 GHz signals. The dual band can make full use of existing positioning information and achieve a better positioning effect than the 2.4 GHz, 5 GHz, and hybrid fingerprints. The function of the normalization algorithm is to eliminate the metrics of the RSS measurements to avoid their influence on the value calculation.
- (2)
- A model construction method is used to build the positioning model of each dual-band fingerprint in this paper. In the proposed method, based on the GPR algorithm and neighborhood fingerprints, the positioning model of the dual-band fingerprint can be constructed. The proposed model construction method can avoid the decreases in the positioning model’s precision as the positioning area increases.
- (3)
- We propose a two-step positioning strategy considering the calculation amount and positioning effect. First, the 5 GHz fingerprint is used to solve a relatively high-precision initial position due to the better stability of the 5 GHz signal than the 2.4 GHz signal, and KNN with low complexity is chosen as the positioning algorithm. Then, the optimal positioning model can be chosen based on the initial position, which is employed to ensure a more accurate position.

## 2. Related Work

## 3. Algorithm

#### 3.1. Normalization Algorithm

#### 3.2. KNN Algorithm

#### 3.3. GPR Algorithm

#### 3.4. Rank Algorithm

#### 3.5. Coverage-Area Algorithm

## 4. The Proposed Method

#### 4.1. Stability Analysis of RSS Measurements of Dual-Band Wi-Fi

#### 4.2. The Traditional Fingerprint

**M**denotes the number of the perceived APs.

#### 4.3. The Proposed Dual-Band Fingerprint

#### 4.4. Overview of the Proposed Method

## 5. Experimental Analysis

#### 5.1. Experimental Environment

^{2}. The blue circle and red rectangle in the Figure represent the RP and test point (TP), respectively. The distance between two adjacent RPs was 1.2 m. There were 10 dual-band Wi-Fi routers in Scenario A, which could all transmit dual-band signals. There were 114 RPs and 28 TPs in Scenario A. The XIAOMI 8 Smartphone was used to gather the data in this scenario.

^{2}, and its length was 16.8 m. The blue rectangle and red circle in Figure 9 represent the RP and TP, respectively. The distance between adjacent RPs was, at maximum, 0.7 m, and the distance between a small number of adjacent RPs was 0.6 m. There were five dual-band Wi-Fi routers with dual-band signal emissions in Scenario B. There were 183 and 24 RPs and TPs in Scenario B, respectively. In this scenario, the HUAWEI P20 Smartphone was used to collect data.

#### 5.2. Experimental Description

#### 5.3. Comparison of Model Precision

#### 5.4. Positioning Effect of the 2.4 GHz, 5 GHz, Hybrid, and Dual-Band Fingerprints in Scenario A

#### 5.5. Positioning Effect of the 2.4 GHz, 5 GHz, Hybrid, and Dual-Band Fingerprints in Scenario B

#### 5.6. Positioning Effect of the Proposed Method in Two Scenarios

## 6. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 7.**The schematic diagram of the model construction, (

**a**) show the model construction of dual-band fingerprint ${R}_{i}$, (

**b**) show the model construction of dual-band fingerprint ${R}_{i+1}$.

Fingerprint | ME | RMSE |
---|---|---|

2.4 GHz fingerprint | 2.318 | 2.793 |

5 GHz fingerprint | 2.509 | 3.011 |

Hybrid fingerprint | 2.238 | 2.922 |

Dual-band fingerprint | 2.020 | 2.404 |

Fingerprint | ME | RMSE |
---|---|---|

2.4 GHz fingerprint | 2.366 | 2.589 |

5 GHz fingerprint | 2.352 | 2.559 |

Hybrid fingerprint | 2.120 | 2.392 |

Dual-band fingerprint | 1.896 | 2.154 |

Method | 50% | 70% | 90% | ME | RMSE |
---|---|---|---|---|---|

KNN | 1.393 | 2.220 | 3.042 | 1.803 | 2.262 |

Rank | 2.425 | 4.573 | 7.328 | 3.651 | 4.876 |

Coverage-area | 2.179 | 3.900 | 6.294 | 3.159 | 3.837 |

GPR | 1.631 | 2.311 | 3.562 | 2.020 | 2.404 |

Proposed method | 0.748 | 1.423 | 2.008 | 1.067 | 1.331 |

Method | 50% | 70% | 90% | ME | RMSE |
---|---|---|---|---|---|

KNN | 1.504 | 1.876 | 3.204 | 1.896 | 2.153 |

Rank | 2.453 | 2.608 | 5.042 | 2.808 | 3.337 |

Coverage-area | 3.023 | 4.808 | 5.760 | 3.613 | 4.047 |

GPR | 1.542 | 2.354 | 3.551 | 1.981 | 2.291 |

Proposed method | 1.037 | 1.432 | 2.249 | 1.432 | 1.712 |

Method | ME | RMSE |
---|---|---|

KNN | 2.278 | 2.677 |

Rank | 3.262 | 4.235 |

Coverage-area | 3.369 | 3.936 |

GPR | 2.002 | 2.353 |

Proposed method | 1.236 | 1.519 |

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**MDPI and ACS Style**

Cao, H.; Wang, Y.; Bi, J.; Sun, M.; Qi, H.; Xu, S.
Fingerprint Positioning Method for Dual-Band Wi-Fi Based on Gaussian Process Regression and K-Nearest Neighbor. *ISPRS Int. J. Geo-Inf.* **2021**, *10*, 706.
https://doi.org/10.3390/ijgi10100706

**AMA Style**

Cao H, Wang Y, Bi J, Sun M, Qi H, Xu S.
Fingerprint Positioning Method for Dual-Band Wi-Fi Based on Gaussian Process Regression and K-Nearest Neighbor. *ISPRS International Journal of Geo-Information*. 2021; 10(10):706.
https://doi.org/10.3390/ijgi10100706

**Chicago/Turabian Style**

Cao, Hongji, Yunjia Wang, Jingxue Bi, Meng Sun, Hongxia Qi, and Shenglei Xu.
2021. "Fingerprint Positioning Method for Dual-Band Wi-Fi Based on Gaussian Process Regression and K-Nearest Neighbor" *ISPRS International Journal of Geo-Information* 10, no. 10: 706.
https://doi.org/10.3390/ijgi10100706