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Enhancement of Localization Systems in NLOS Urban Scenario with Multipath Ray Tracing Fingerprints and Machine Learning^{ †}

^{*}

^{†}

## Abstract

**:**

## 1. Introduction

#### 1.1. Review of Passive Emitter Localization Techniques

#### 1.2. Research Contributions

## 2. Problem Formulation

#### 2.1. Data Model for TDOA Localization

**x**given the ${\mathbf{r}}_{TDOA}$, assuming that ${\mathbf{n}}_{TDOA}$ is zero-mean and Gaussian noise in the range measurements with Probability Density Function (PDF), for ${\mathbf{r}}_{TDOA}$, denoted by $p\left\{{\mathbf{r}}_{TDOA}\right\}$ with the following structure:

#### 2.2. Source Localization Algorithms

#### 2.3. Effect of NLOS in TDOA Estimation

## 3. Proposed Method

#### 3.1. Multipath Dataset Using Ray Tracing

#### 3.2. Channel Impulse Response Estimation

#### 3.3. Localization Using NLOS Ray-Tracing Fingerprints

#### 3.4. Machine Learning in Multipath Fingerprint

- Jupyter Notebook version 5.1.0 (http://jupyter.org);
- Python version 3.6.3 (https://www.python.org);
- Python numpy version 1.13.3 (http://www.numpy.org);
- Tensorflow version 1.4.0 (http://tensorflow.org); and
- CentOS version 7.4.1708 (https://www.centos.org) in Google cloud (http://cloud.google.com).

## 4. Experimental Setup

#### 4.1. Multipath Fingerprint Dataset Preprocessing

#### 4.2. Hypothesis and Loss Function

#### 4.3. Training and Model Generation

#### 4.4. Results

## 5. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## Abbreviations

AOA | Angle of Arrival |

CBB | Complex Baseband |

CIR | Channel Impulse Response |

CRB | Cramer-Rao Bound |

DMC | Dense Multipath Components |

DOA | Direction of Arrival |

EKF | Extended Kalman Filter |

FDOA | Frequency Difference of Arrival |

FIM | Fisher Information Matrix |

LS | Least Squares |

LLS | Linear Least Squares |

KF | Kalman Filter |

ML | Maximum Likelihood |

NLOS | Non-Line-of-sight |

NLS | Nonlinear Least Squares |

TDOA | Time-difference of Arrival |

RSS | Received Signal Strength |

RX | Receiver |

SC | Specular Components |

SDP | Semidefinite Programming |

SVM | Support Vector Machine |

TMA | Target Motion Analysis |

TOA | Time of Arrival |

TTW | Through-the-wall |

TX | Transmitter |

WLLS | Weighted Linear Least Squares |

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Algorithm: TDOA with RT Fingerprints. | |
---|---|

1: | Begin: |

2: | From a deployed TDOA System: |

$\phantom{\rule{0.166667em}{0ex}}\xb7\phantom{\rule{0.166667em}{0ex}}$ Record the signal, extract the CBB; | |

$\phantom{\rule{0.166667em}{0ex}}\xb7\phantom{\rule{0.166667em}{0ex}}$ Extract a “windows” in time domain of the CBB signal; | |

$\phantom{\rule{0.166667em}{0ex}}\xb7\phantom{\rule{0.166667em}{0ex}}$ Using the sample, extract TDOA vector $[{\tau}_{21},{\tau}_{31},{\tau}_{41}]$; | |

$\phantom{\rule{0.166667em}{0ex}}\xb7\phantom{\rule{0.166667em}{0ex}}$ Estimate the emitter position. | |

3: | Perform Ray Tracing Simulation of the Scenario with: |

$\phantom{\rule{0.166667em}{0ex}}\xb7\phantom{\rule{0.166667em}{0ex}}$ TDOA Sensor Positions, | |

$\phantom{\rule{0.166667em}{0ex}}\xb7\phantom{\rule{0.166667em}{0ex}}$ Emitter position vector $\mathit{x}$, | |

$\phantom{\rule{0.166667em}{0ex}}\xb7\phantom{\rule{0.166667em}{0ex}}$ Buildings descriptions. | |

4: | With RT output collect Amplitude (${\alpha}_{i}$) and Delay (${\tau}_{i}$) |

5: | Build a Neural Network (NN) with: |

$\phantom{\rule{0.166667em}{0ex}}\xb7\phantom{\rule{0.166667em}{0ex}}$ 40 Input: 5 (${\alpha}_{i},{\tau}_{i}$) for each TDOA Sensor; | |

$\phantom{\rule{0.166667em}{0ex}}\xb7\phantom{\rule{0.166667em}{0ex}}$ 8 Neurons in the hidden layer; | |

$\phantom{\rule{0.166667em}{0ex}}\xb7\phantom{\rule{0.166667em}{0ex}}$ weights and hyper-parameter for “Supervised Learning”. | |

$\phantom{\rule{0.166667em}{0ex}}\xb7\phantom{\rule{0.166667em}{0ex}}$ 3 Outputs: Emitter Position ${[{X}_{e},{Y}_{e},{Z}_{e}]}^{T}$. | |

6: | Refine the NN: |

$\phantom{\rule{0.166667em}{0ex}}\xb7\phantom{\rule{0.166667em}{0ex}}$ Record from the Calibration; | |

$\phantom{\rule{0.166667em}{0ex}}\xb7\phantom{\rule{0.166667em}{0ex}}$ Perform CIR estimation to extract (${\alpha}_{i},{\tau}_{i}$) from calibration; | |

$\phantom{\rule{0.166667em}{0ex}}\xb7\phantom{\rule{0.166667em}{0ex}}$ Perform hyper-parameters refinement; | |

$\phantom{\rule{0.166667em}{0ex}}\xb7\phantom{\rule{0.166667em}{0ex}}$ Adjust the dataset of Machine Learning Engine. | |

7: | Perform the CIR of the emitter: |

$\phantom{\rule{0.166667em}{0ex}}\xb7\phantom{\rule{0.166667em}{0ex}}$ Refine the NN adjustments from calibration. | |

8: | Perform the Position Estimation |

Apply K-nearest neighbor(KNN) method and NN refined. | |

9 | Repeat the algorithm from step 4 |

with a new integration windows | |

untill the end of the recording signal. | |

10: | Perform the joint position estimation |

with $\mathit{P}({\mathit{\mu}}_{\mathit{TDOA}},{\mathit{\sigma}}_{\mathit{TDOA}})\cap \mathit{P}({\mathit{\mu}}_{\mathit{RT}},{\mathit{\sigma}}_{\mathit{RT}})$. |

TDOA | Ground Truth GPS | Measurements | Proposed Method ($\mathsf{\mu}$s) | Error Reduction (Improment) | |
---|---|---|---|---|---|

Mean ($\mathsf{\mu}$s) | Variance ($\mathsf{\mu}$s) | ||||

${\tau}_{21}$ | 0.333 | 0.114 | 0.0341 | 0.344 | $94\%$ |

${\tau}_{31}$ | 0.27 | 1.76 | 0.0303 | 0.266 | $96\%$ |

${\tau}_{41}$ | 0.266 | 0.15 | 0.0157 | 0.238 | $79\%$ |

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## Share and Cite

**MDPI and ACS Style**

N. de Sousa, M.; S. Thomä, R.
Enhancement of Localization Systems in NLOS Urban Scenario with Multipath Ray Tracing Fingerprints and Machine Learning. *Sensors* **2018**, *18*, 4073.
https://doi.org/10.3390/s18114073

**AMA Style**

N. de Sousa M, S. Thomä R.
Enhancement of Localization Systems in NLOS Urban Scenario with Multipath Ray Tracing Fingerprints and Machine Learning. *Sensors*. 2018; 18(11):4073.
https://doi.org/10.3390/s18114073

**Chicago/Turabian Style**

N. de Sousa, Marcelo, and Reiner S. Thomä.
2018. "Enhancement of Localization Systems in NLOS Urban Scenario with Multipath Ray Tracing Fingerprints and Machine Learning" *Sensors* 18, no. 11: 4073.
https://doi.org/10.3390/s18114073