Indoor Localization using RSSI and Artificial Neural Network

Puckdeevongs, Apiruk

doi:10.1109/ieecon51072.2021.9440373

Cited by 9 publications

(5 citation statements)

References 8 publications

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“…RSSI-based fingerprinting is commonly used in wireless systems that have rich AP distributions such as Wireless Sensor Networks (WSNs) [64][65][66], Wi-Fi networks [67,68], or Distributed Massive MIMO (DM-MIMO) systems [69,70]. Since the RSSI provides a single measurement from the BS or AP, multiple APs are required to generate a unique fingerprint.…”

Section: Fingerprinting-based Localizationmentioning

confidence: 99%

Localization Techniques in Multiple-Input Multiple-Output Communication: Fundamental Principles, Challenges, and Opportunities

Vuckovic,

Rahanvard

2023

MIMO Communications - Fundamental Theory, Propagation Channels, and Antenna Systems

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This chapter provides an overview of localization techniques in Multiple-Input Multiple-Output (MIMO) communication systems. The chapter mainly focuses on sub-6 GHz and mmWave bands. MIMO technology enables high-capacity wireless communication, but also presents challenges for localization due to the complexity of the signal propagation environment. Various methods have been developed to overcome these challenges, which utilize side information such as the map of the area, or techniques such as Compressive Sensing (CS), Deep Learning (DL), Gaussian Process Regression (GPR), or clustering. These techniques utilize wireless communication parameters such as Received Signal Strength Indicator (RSSI), Channel State Information (CSI), Angle-Delay-Profile (ADP), Angle-of-Departure (AoD), Angle-of-Arrival (AoA), or Time-of-Arrival (ToA) as inputs to estimate the user’s location. The goal of this chapter is to offer a comprehensive understanding of MIMO localization techniques, along with an overview of the challenges and opportunities associated with them. Furthermore, it also aims to provide the theoretical background on channel models and wireless channel parameters required to understand the localization techniques.

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Section: Fingerprinting-based Localizationmentioning

confidence: 99%

Localization Techniques in Multiple-Input Multiple-Output Communication: Fundamental Principles, Challenges, and Opportunities

Vuckovic,

Rahanvard

2023

MIMO Communications - Fundamental Theory, Propagation Channels, and Antenna Systems

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“…In [8], the author used a ANN to calculate the location of the target within a laboratory. The wireless technology used was Wifi and an ESP8266 board as the target node to collect RSSI values.…”

Section: Log Distance Path Loss Model (Ldpl)mentioning

confidence: 99%

“…Unlike the proposal in [8], which uses Wifi to capture RSSI, this work uses Bluetooth Beacon, a solution focused on lowpower consumption. In [9], the authors also use Bluetooth Beacons and ANN to determine the location.…”

Section: Log Distance Path Loss Model (Ldpl)mentioning

confidence: 99%

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Evaluation of artificial neural networks for indoor positioning using Bluetooth Beacons

Vanzin

Postal

Rodrigues

et al. 2022

Anais Estendidos Do XII Simpósio Brasileiro De Engenharia De Sistemas Computacionais (SBESC Estendido 2022)

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Indoor positioning opens up opportunities for a wide range of applications, including active marketing, accessibility and security. Although GPS (Global Positioning System) is widely used for outdoor location, it is inaccurate and in some cases unavailable indoors. One of the solutions is to use Bluetooth Beacons to determine the distance between the device and the beacon indoors using the Received Signal Strength Indicator (RSSI). The location of the object in the environment can be determined using at least three beacons and methods such as trilateration. This work aims to evaluate the use of artificial neural networks (ANN) to determine the distance and location of the laptop in an indoor environment. A first experiment compares the Log Distance Path Loss (LDPL) model and the ANN to determine the distance between the beacon and a laptop. A second experiment compares which method is best to determine the position of the laptop in a room. The following methods were evaluated: a) trilateration with distance calculation using the LDPL method; b) trilateration with distance calculation using an ANN; and c) position determination using an ANN. The results show that RSSI values can vary due to obstacles and the position of the antenna between the beacon and the laptop.

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“…Since the RSS provides a single measurement from the BS or Access Point (AP), multiple APs are required to generate a unique fingerprint. Therefore, RSS fingerprinting is used in wireless system that have rich AP distributions such as Wireless Sensor Networks (WSNs) [9], [10], WiFi networks [11]- [14], or Distributed Massive MIMO (DM-MIMO) systems [15], [16].…”

Section: Introductionmentioning

confidence: 99%

MAP-CSI: Single-site Map-Assisted Localization Using Massive MIMO CSI

Vuckovic

Hejazi

Rahnavard

2021

2021 IEEE Global Communications Conference (GLOBECOM)

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This work presents a date-driven user localization framework for single-site massive Multiple-Input-Multiple-Output (MIMO) systems. The framework is trained on a geo-tagged Channel State Information (CSI) dataset. Unlike the state-of-the-art Convolutional Neural Network (CNN) models, which require large training datasets to perform well, our method is specifically designed to operate with small-scale training datasets. This makes our approach more practical for real-world scenarios, where collecting a large amount of data can be challenging. Our proposed FC-AE-GPR framework combines two components: a Fully-Connected Auto-Encoder (FC-AE) and a Gaussian Process Regression (GPR) model. Our results show that the GPR model outperforms the CNN model when presented with small training datasets. However, the training complexity of GPR models can become an issue when the input sample size is large. To address this, we propose using the FC-AE to reduce the sample size by encoding the CSI before training the GPR model. Although the FC-AE model may require a larger training dataset initially, we demonstrate that the FC-AE is scenario independent. This means that it can be utilized in new and unseen scenarios without prior retraining. Therefore, adapting the FC-AE-GPR model to a new scenario requires only retraining the GPR model with a small training dataset.

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Indoor Localization using RSSI and Artificial Neural Network

Cited by 9 publications

References 8 publications

Localization Techniques in Multiple-Input Multiple-Output Communication: Fundamental Principles, Challenges, and Opportunities

Localization Techniques in Multiple-Input Multiple-Output Communication: Fundamental Principles, Challenges, and Opportunities

Evaluation of artificial neural networks for indoor positioning using Bluetooth Beacons

MAP-CSI: Single-site Map-Assisted Localization Using Massive MIMO CSI

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