Adaptive probabilistic model using angle of arrival estimation for IoT indoor localization

BniLam, Noori; Ergeerts, Glenn; Subotić, Dragan; Steckel, Jan; Weyn, Maarten

doi:10.1109/ipin.2017.8115864

Cited by 22 publications

(17 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The subspace-based algorithms for narrowband Angle-of-Arrival (AoA) estimation have received more attention due to their high accuracy. These algorithms are suitable for Internet-of-Thing (IoT) applications [1]. Bluetooth Low Energy (BLE) is the most popular wireless connection technology for IoT sensor networks thanks to its low power consumption, low cost, and high availability [2].…”

Section: Introductionmentioning

confidence: 99%

Experimental Demonstration of AoA Estimation Uncertainty for IoT Sensor Networks

Monfared

Nguyen

Vorst

et al. 2020

2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring)

View full text Add to dashboard Cite

In practice, the subspace-based algorithms such as Multiple Signal Classification (MUSIC) suffer from sensitivity to antenna-array response errors and therefore they require the assessment of the calibration gain and phase perturbations. This paper evaluates experimentally the accuracy of Angle-of-Arrival (AoA) estimation based on the MUSIC algorithm only coming from these perturbations in the context of Internet-of-Thing (IoT) applications. First of all, a new Over-the-Air (OTA) calibration method is proposed and gain and phase uncertainties are investigated. The impact of these uncertainties on the accuracy of AoA estimation is then studied and compared with the theoretical analysis. The experimental results show that the calibration errors coming from hardware imperfections can cause some degrees of uncertainty in AoA estimation.

show abstract

Section: Introductionmentioning

confidence: 99%

Experimental Demonstration of AoA Estimation Uncertainty for IoT Sensor Networks

Monfared

Nguyen

Vorst

et al. 2020

2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring)

View full text Add to dashboard Cite

show abstract

“…Sigfox and NB-IoT networks, on the other hand, provide localization solutions based on the RSS values [16]. Even though the AoA estimation techniques have the potential to provide an accurate localization solution for IoT applications [17], commercially available AoA-based localization systems (to the best of the authors' knowledge) do not yet exist for IoT applications. AoA-based localization require multiple spatially distributed AoA estimation systems to provide a location estimate of the transmitting device.…”

Section: The Localization Approachmentioning

confidence: 99%

AoA-Based Localization System Using a Single IoT Gateway: An Application for Smart Pedestrian Crossing

et al. 2021

Self Cite

View full text Add to dashboard Cite

During the last decade, the concept of smart cities is gaining popularity, and many cities all around the globe are adopting it. Lately, the Smart Pedestrian Crossing (SPC) idea has become an important part of the smart city concept. The main objective of the SPC is to achieve a safe and smooth traffic flow of pedestrians and vehicles in the smart city. In this paper, we present a simple yet efficient localization system which is suitable for locating people over a pedestrian crossing. By adopting the proposed localization system, the people flow over the pedestrian crossing can be observed. Consequently, a safe and efficient traffic flow in the smart city can be achieved. The proposed system utilizes Internet of Things (IoT) transceivers that are carried by the pedestrians or embedded in the pedestrians' smart devices. These transceivers can be activated to transmit a signal every few seconds when the pedestrian passes over a pedestrian crossing. These periodic signals will be received by a single Angle of Arrival (AoA) estimation system to estimate the direction of the pedestrian. Furthermore, the pedestrian direction and the environmental features of the pedestrian crossings are utilized in a 1D particle filter to estimate accurately the pedestrian's location. The proposed system has been validated experimentally in the smart zone of Antwerp city. The experimental results reveal that the proposed localization system can provide an accurate, cost effective and reliable localization solution to this specific problem without violating the pedestrian privacy. INDEX TERMS AoA, angle of arrival, DoA, direction of arrival, Internet of Things, IoT, environmental features localization, smart pedestrian crossing, smart cities, particle filter, SAGE algorithm.

show abstract

“…In this section, we present the Fixed Probabilistic Model (FPM). The FPM can be used to localize a transmitting device based on the AoA estimations of the received signal [5]. The term ''fixed'' has been added to emphasize that the probabilistic model is independent of the AoA estimation process (i.e.…”

Section: Fixed Probabilistic Model (Fpm)mentioning

confidence: 99%

“…estimation technique) provide the direction of the received signals with respect to the array antenna system. Combining the direction information, either by a triangulation process [4] or probabilistic models [5], can provide a location estimate of the transmitting device.…”

Section: Introductionmentioning

confidence: 99%

ANGLE: ANGular Location Estimation Algorithms

et al. 2020

Self Cite

View full text Add to dashboard Cite

In this paper, we present two localization algorithms that exploit the Angle of Arrival (AoA) parameters of the received signal. The proposed ANGular Location Estimation (ANGLE) algorithms utilize a probabilistic model to describe the angular response of the received signal. Consequently, the ANGLE algorithms can estimate the location of a transmitter using a single step Hadamard product. The first algorithm utilizes a Single Sample of the received signal (ANGLE-SS). The second algorithm, on the other hand, employs the signal Subspace Decomposition technique (ANGLE-SD). The localization capabilities of the ANGLE algorithms have been experimentally investigated in an office environment. The performances of the ANGLE algorithms have been validated against the performances of several AoA-based localization systems. The experimental results show that the ANGLE-SD algorithm outperforms all the studied AoA-based localization systems. The ANGLE-SS algorithm, on the other hand, outperforms every localization system that utilizes less than 50 samples of the received signal. The ANGLE algorithms are flexible, generic and computationally very efficient. These features allow the ANGLE algorithms to be easily deployed in any existing AoA-based localization system. INDEX TERMS Angle of arrival, AoA, direction of arrival, DoA, AoA-based localization systems, indoor localization systems.

show abstract

Adaptive probabilistic model using angle of arrival estimation for IoT indoor localization

Cited by 22 publications

References 11 publications

Experimental Demonstration of AoA Estimation Uncertainty for IoT Sensor Networks

Experimental Demonstration of AoA Estimation Uncertainty for IoT Sensor Networks

AoA-Based Localization System Using a Single IoT Gateway: An Application for Smart Pedestrian Crossing

ANGLE: ANGular Location Estimation Algorithms

Contact Info

Product

Resources

About