# A Robust Noise Mitigation Method for the Mobile RFID Location in Built Environment

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## Abstract

**:**

## 1. Introduction

## 2. Related Works

#### 2.1. RFID-Based Location

#### 2.2. RFID Noise Mitigation Methods

## 3. Mobile Localization and Problem Formulation

#### 3.1. Mobile Localization Method

_{i}, y

_{i}). RFID reader ranges the distance r

_{i}between a reader and target tag with unknown coordinates denoted as (x

_{0}, y

_{0}). When moving of RFID reader, many groups of value are measured. The target tag location of (x

_{0}, y

_{0}) will be computed with the trilateration algorithm.

#### 3.2. Noise Influence

_{p}, y

_{p}).

## 4. A RANSAC Based Noise Mitigation Method

#### 4.1. Delta Filter for RFID Measurement Data

Algorithm 1: Delta filter algorithm | |

Input: GNSS signal data, RFID ranging data | |

Output: Satisfied and well-conditioned triangle data | |

1 | setp = point coordinate of the new point from GNSS signal data |

2 | setr = distance from RFID ranging signal data |

3 | For each point in saved reader location points set do |

4 | set p_{i} = point coordinate of points set |

5 | set r_{1} = distance between p_{i} and tag |

6 | set r = distance between p and tag_{2} |

7 | set r_{3} = distance between p_{i} and p |

8 | calculate vertex angle of tag point from r_{1}, r_{2}, and r_{3} |

9 | if the value of the angle is within (30,120) then |

10 | The p, p_{i} and tag point can build a well-conditioned triangle, |

11 | then add the triangle data (p, p_{i}, target point) to a data set for the next process |

12 | else |

13 | continuous the next loop |

14 | end if |

15 | End for |

16 | Add (p, r) to reader location points set for next reader location processing |

#### 4.2. A RANSAC-Based Robust Noise Detection

#### 4.2.1. Making Hypothesis for the Mobile Localization

_{p}, y

_{p}) is the unknown variable. Therefore, it is:

_{p}, y

_{p}) can be achieved.

#### 4.2.2. Parameters Definition for Verification

## 5. Experiment and Result

#### 5.1. Experiment Setup

#### 5.2. Field Test Result

## 6. Comparison with Existing Methods

## 7. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**The workflow of mobile localization scheme with Global Network Satellite System (GNSS) and radio-frequency identification (RFID).

**Figure 4.**The overview of the experiment. (

**a**) is the sketch of the experiment; (

**b**) is the tag placement scenario; (

**c**) is the localization devices.

**Figure 8.**Cumulative distribution of localization error in circle route and line route. (

**a**) Circle route; (

**b**) Line route.

WCL | k-Means | LMS | LMedS | SVR | RANSAC | |
---|---|---|---|---|---|---|

Line Route | 3.6293 | 3.6957 | 4.5916 | 4.3050 | 3.2740 | 2.6529 |

Circle Route | 1.6345 | 3.2779 | 2.2975 | - ^{1} | 1.3573 | 1.2605 |

^{1}The result cannot computation owing to the computational complexity.

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## Share and Cite

**MDPI and ACS Style**

Jing, C.; Sun, T.; Chen, Q.; Du, M.; Wang, M.; Wang, S.; Wang, J.
A Robust Noise Mitigation Method for the Mobile RFID Location in Built Environment. *Sensors* **2019**, *19*, 2143.
https://doi.org/10.3390/s19092143

**AMA Style**

Jing C, Sun T, Chen Q, Du M, Wang M, Wang S, Wang J.
A Robust Noise Mitigation Method for the Mobile RFID Location in Built Environment. *Sensors*. 2019; 19(9):2143.
https://doi.org/10.3390/s19092143

**Chicago/Turabian Style**

Jing, Changfeng, Tiancheng Sun, Qiang Chen, Mingyi Du, Mingshu Wang, Shouqing Wang, and Jian Wang.
2019. "A Robust Noise Mitigation Method for the Mobile RFID Location in Built Environment" *Sensors* 19, no. 9: 2143.
https://doi.org/10.3390/s19092143