# Deep Learning-Based Indoor Localization Using Multi-View BLE Signal

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## Abstract

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## 1. Introduction

- ML-powered BLE-based indoor positioning via multiple anchor AoA estimation using both raw IQ values and RSSI estimates is proposed for the first time;
- A range of novel deep learning architectures, including fully connected multilayer perceptrons and CNNs are proposed and studied regarding their pros and cons;
- Joint anchor AoA estimates allowing distributed processing across the anchors is studied, to the best of our knowledge, for the first time. In particular, tuples of APs are grouped in smaller models that are then combined to produce the final prediction. Thus, hardware for a single computational expensive unit is replaced by less computationally demanding units distributed among the APs, facilitating embedded implementations;
- It is shown that deep learning methods yield robust indoor localization generalization, given environmental changes, e.g., different LOS-blocking obstacles and altered AP arrangements. To the best of our knowledge, no other study for indoor localization based on machine learning evaluates the generalizability of its models in different environments than the ones used in training;
- A data augmentation strategy is introduced that allows for reducing the training data size with minimal impact on performance. The performance improvement potential of joint anchor estimates vs. single anchors is also studied;
- A novel high spatial resolution dataset with multiple furniture configurations produced with realistic ray-tracing simulations is provided in open access mode.

## 2. Materials and Methods

#### 2.1. Theoretical Foundation

_{ref}is the reference RSSI value measured at a distance of 1 m and n is the attenuation constant.

_{i}, where ${\theta}_{i}$ is the complementary of the polar angle of the spherical coordinate system and ${\mathit{a}}_{i}=\left[{x}_{ai},{y}_{ai},{z}_{ai}\right]$ is the position of AP

_{i}. The pairs of equations that form the system correspond to pairs of planes whose intersection is the direction of arrival line. Alternatives, e.g., the one in [30], could also be used for the AoA to location conversion.

#### 2.2. System Setup and Data Preprocessing

#### 2.3. NN Model Architectures

- Independent APs: In this architecture (Figure 1b), each AP has its own model for AoA estimates and the models are trained independently from each other. Each AP model input consists of three feature vectors of length 10, one per channel. Each vector consists of 9 IQ values and the channel RSSI value. This vector is hereafter referred to as channel signal vector. Each channel signal vector is fed to a different 3-layer neural network that produces a 5-dimensional latent representation. The 3 latent representations are then concatenated together with the RSSIs and fed to a 3-layer channel fusion MLP, which outputs the 2 angular directions (azimuth and elevation) of the AoA. In the sequel, we refer to this module as the channel fusion module (Figure 1a). Note that this architecture along with the ones presented later on, are directly extendable to more than 3 BLE channels and are not bound to the configuration that is chosen here for demonstration purposes. An advantage of the independent APs architecture is that the computational requirements are distributed across the anchors. However, this architecture does not exploit the fact that the APs are placed in fixed positions in a specific room, so the signal received by an AP from a tag placed anywhere in the room also conveys information about the AoA of this signal to the rest of the APs. This shortcoming is addressed by joint AP architectures discussed next.
- Fully joint APs: This architecture aims to jointly estimate the AoA values of all APs using the respective received signals. Before going into the details for this architecture, let us first describe the channel and AP fusion module shown in Figure 2a, which is the main building block of this architecture and the following ones. This NN module first computes a latent representation for each channel and for a set of APs of size k, where k is a hyperparameter. To this end, the channel signal vectors of all anchors are concatenated per channel and fed as input to 3 different 3-layer MLPs with layer sizes 60, 40 and 12. Then the outputs of these MLPs are concatenated and, together with all the channel RSSIs, are fed into another 2-layer fusion MLP with layers of size 64 and 8, respectively. The fully joint AP architecture, shown in Figure 2b, is essentially the channel and AP fusion module. Accordingly, the fusion MLP output layer has 8 nodes to produce the final AoA estimates for all APs (2 angular directions per AoA × 4 APs). An advantage of this architecture compared to the independent AP one is the performance improvement, as will also be discussed in Section 3. A disadvantage is the lack of the distributed processing capability, which means that a powerful enough central computing unit is required to collect all signals and run the model.
- Tuples of APs: This architecture aims to combine the best features of the two aforementioned architectures, namely, high performance and distributed computing potential. The idea is to jointly tackle k-combinations with repetition. Taking for example k = 3, exemplary possible AP combinations are ABC, ABD, ACD and BCD. Then, the corresponding triplets of APs architecture is shown in Figure 3, where the channel signal vectors of the four distinct triplets feed corresponding channel and AP fusion modules. In this case the three different MLPs of the channel and AP fusion module consist of 2-layers with sizes 24 and 9 and the Fusion MLP consists of 2-layers as well, but this time with sizes 27 and 12. Subsequently, the latent representations from the outputs of these models are concatenated and fed to a final 3-layer combination fusion MLP, with sizes 32, 16 and 8. The final output are the 8 AoA values for all 4 APs. As shown in Section 3, the performance of this architecture is similar, if not better, to that of the fully joint setup. In addition, the computational complexity can be distributed across the four APs if edge computing units embedded in the APs perform the computations of each channel and AP fusion module in parallel. The computations for the MLP that performs the final fusion along with the LS-based estimates of the positioning, still need to be performed subsequently. Similar to the triplets of APs architecture, one can also define the pairs of APs architecture.
- CNN-based joint APs: This is an alternative joint architecture that operates on measurements taken from all four APs simultaneously using convolutional neural networks (CNNs), as shown in Figure 4. A major difference compared to the architecture of fully joint APs is that the channel and APs fusion module has been replaced by convolutional layers. To achieve this, the channel signal vectors are rearranged to form a 2D image-like representation of size 10 × 4 × 3, where the dimensions correspond to the APs, the channel signal vectors and the BLE channels, respectively. The 2 convolutional layers use kernels of size 4 × 1 and 3 × 2, respectively, and are followed by 3 fully connected layers, as shown in Figure 4.

#### 2.4. Simulation Environment

- The no LoS-blocking furniture case (referred to as “No Furniture”);
- Having one piece of LoS-blocking furniture, covering 0.5% of the room’s area (referred to as “Low Furniture”);
- Having three pieces of LoS-blocking furniture, covering 1.5% of the room’s area (referred to as “Mid Furniture”);
- Having six pieces of LoS-blocking furniture, covering 3% of the room’s area (referred to as “High Furniture”).

## 3. Results

#### 3.1. Performance on a Fixed Environment

#### 3.2. Generalization Performance on Environment Configurations Not Seen during Training

#### 3.3. Study of the Error Spatial Distribution

#### 3.4. Study of Model Generalization against Moderate AP Displacements

#### 3.5. Impact of Model Size and Training Dataset Size

## 4. Discussion

## 5. Conclusions

## Supplementary Materials

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## Abbreviations

BLE | Bluetooth low energy |

Τag | Transmitter |

AP | Anchor point |

RSSI | Received signal strength indicator |

IQ | In-phase and quadrature-phase |

AoA | Angle of arrival |

ML | Machine learning |

LS | Least squares |

IPS | Indoor positioning system |

UWB | Ultra-wide band |

CTE | Constant tone extension |

ESPRIT | Estimation of signal parameters via rotational invariance technique |

MUSIC | Multiple signal classification |

PDDA | Propagator direct data acquisition |

Knn | k Nearest Neighbors |

NN | Neural network |

LoS | Line of sight |

NLoS | No line of sight |

CNN | Convolutional neural network |

MEDE | Mean Euclidean distance error |

MAE | Mean Absolute Error |

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**Figure 1.**(

**a**) Channel fusion module; (

**b**) independent AP architecture. Layer sizes for each MLP are shown in parentheses.

**Figure 6.**(

**a**) Measured RSSI values by a single AP for 3 different rooms configurations and (

**b**) cosine distance of IQ features between the low furniture room configuration and the rest of the furniture configurations.

**Figure 7.**MEDE of the CNN-based joint model on different rooms. PDDA methods performance in each room is included for reference.

**Figure 8.**MEDE of the pairs of APs model on different rooms. PDDA methods performance in each room is included for reference.

**Figure 9.**Spatial distribution of Euclidean error on the high furniture room of (

**a**) PDDA; (

**b**) CNN-based joint model; (

**c**) pairs of APs Model.

**Figure 12.**Training (red dots), validation (green dots) and test locations (blue dots) for training set of size (

**a**) 140 and (

**b**) 25.

**Figure 13.**MEDE of fully joint model and CNN-based joint model across all rooms with number of parameters.

**Table 1.**MEDE ± standard deviation comparison of each model and PDDA when evaluated in 2123 spatially distributed locations that have not participated in model training of the low furniture and the high furniture rooms.

Model | MEDE in Low Furniture (Meters) | MEDE in High Furniture (Meters) | ||
---|---|---|---|---|

Non-Augmented | Augmented | Non-Augmented | Augmented | |

Independent | 1.03 ± 0.78 | 0.96 ± 0.74 | 1.10 ± 0.86 | 1.08 ± 0.86 |

Fully Joint | 0.75 ± 0.53 | 0.65 ± 0.47 | 0.86 ± 0.60 | 0.75 ± 0.52 |

Triplets of APs | 0.82 ± 0.52 | 0.70 ± 0.48 | 0.96 ± 0.60 | 0.84 ± 0.57 |

Pairs of APs | 0.76 ± 0.50 | 0.69 ± 0.48 | 0.91 ± 0.57 | 0.71 ± 0.48 |

CNN-based Joint | 0.76 ± 0.49 | 0.61 ± 0.45 | 0.87 ± 0.53 | 0.69 ± 0.50 |

PDDA | 1.14 ± 0.95 | 1.14 ± 0.95 | 1.30 ± 1.14 | 1.30 ± 1.14 |

Model | MAE in High Furniture (°) | |||
---|---|---|---|---|

AP_{1} | AP_{2} | AP_{3} | AP_{4} | |

Independent | 5.64 ± 8.33 | 6.87 ± 7.29 | 6.27 ± 8.22 | 6.2 ± 9.37 |

Fully Joint | 4.18 ± 7.80 | 4.44 ± 5.51 | 3.89 ± 4.79 | 4.38 ± 7.06 |

Triplets of APs | 4.54 ± 6.96 | 4.62 ± 6.29 | 4.26 ± 5.34 | 4.82 ± 6.92 |

Pairs of APs | 4.02 ± 7.35 | 4.25 ± 5.48 | 3.99 ± 5.15 | 4.03 ± 6.50 |

CNN-based Joint | 4.03 ± 6.76 | 3.89 ± 4.86 | 3.50 ± 4.8 | 4.31 ± 6.98 |

PDDA | 5.80 ± 9.14 | 8.38 ± 10.34 | 5.86 ± 9.57 | 7.40 ± 11.26 |

Model | MEDE (m) | |
---|---|---|

Left Half (Furniture Dense) | Right Half (Furniture Free) | |

Independent | 1.12 | 1.13 |

Fully Joint | 0.80 | 0.79 |

Triplets of APs | 0.79 | 0.88 |

Pairs of APs | 0.80 | 0.88 |

CNN-based Joint | 0.73 | 0.85 |

PDDA | 1.23 | 1.36 |

Model | Description | Advantages | Disadvantages |
---|---|---|---|

Independent | AoA computed independently by each AP | Simple implementation, Distributed computation | Lower accuracy compared to the rest of the models |

Fully Joint | Joint estimation of all AoAs by a single ML model | High accuracy | All raw data need to be transferred to a central unit for computation |

Tuples of APs | Joint estimation of AoAs by forming groups of APs | High accuracy, Distributed computation | Performance degrades faster compared to the rest of the models when lower complexity NN configurations are adopted. |

CNN-based Joint | Joint estimation of AoAs by a single CNN | Highest accuracy, Most robust approach to complexity reduction and to smaller training size | All raw data need to be transferred to a central unit for computation |

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## Share and Cite

**MDPI and ACS Style**

Koutris, A.; Siozos, T.; Kopsinis, Y.; Pikrakis, A.; Merk, T.; Mahlig, M.; Papaharalabos, S.; Karlsson, P.
Deep Learning-Based Indoor Localization Using Multi-View BLE Signal. *Sensors* **2022**, *22*, 2759.
https://doi.org/10.3390/s22072759

**AMA Style**

Koutris A, Siozos T, Kopsinis Y, Pikrakis A, Merk T, Mahlig M, Papaharalabos S, Karlsson P.
Deep Learning-Based Indoor Localization Using Multi-View BLE Signal. *Sensors*. 2022; 22(7):2759.
https://doi.org/10.3390/s22072759

**Chicago/Turabian Style**

Koutris, Aristotelis, Theodoros Siozos, Yannis Kopsinis, Aggelos Pikrakis, Timon Merk, Matthias Mahlig, Stylianos Papaharalabos, and Peter Karlsson.
2022. "Deep Learning-Based Indoor Localization Using Multi-View BLE Signal" *Sensors* 22, no. 7: 2759.
https://doi.org/10.3390/s22072759