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In an application involving Autonomous Underwater Vehicles (AUV) it is important to track the trajectory and spatially correlate the collected data. Relying on an Inertial Navigation System (INS) while factoring in the initial AUV position would not suffice given the major accumulated errors. Employing surface nodes is a logistically complicated option, especially for missions involving emerging events. This paper proposes a novel localization approach that offers both agility and accuracy. The idea is to exploit a communication mechanism across the air-water interface. In particular, we employ an airborne unit, e.g., a drone, that scans the area of interest and uses visual light communication (VLC) to reach the AUV. In essence, the airborne unit defines virtual anchors with known GPS coordinates. The AUV uses the light intensity of the received VLC transmissions to estimate the range relative to the anchor points and then determine its own global coordinates at various time instances. The proposed approach is validated through extensive simulation experiments. The simulation results demonstrate the viability of our approach and analyze the effect of the VLC parameters.
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Machine learning regression-based RETRO-VLP for real-time and stabilized indoor positioning
Many real-world applications require real-time and robust positioning of Internet of Things (IoT) devices. In this context, visible light communication (VLC) is a promising approach due to its advantages in terms of high accuracy, low cost, ubiquitous infrastructure, and freedom from RF interference. Nevertheless, there is a growing need to improve positioning speed and accuracy. In this paper, we propose and prototype a VLC-based positioning solution using retroreflectors attached to the IoT device of interest. The proposed algorithm uses the retroreflected power received by multiple photodiodes to estimate the euclidean and directional coordinates of the underlying IoT device. In particular, the relative relationship between reflected light magnitude and reflected power is used as input to trainable machine learning regression models. Such models are trained to estimate the coordinates. The proposed algorithm excels in its simplicity and fast computation. It also reduces the need for sensory devices and active operation. Additionally, after regression, Kalman filtering is applied as a post-processing operation to further stabilize the obtained estimates. The proposed algorithm is shown to provide stable, accurate, and fast. This has been verified by extensive experiments performed on a prototype in real-world environments. Experiments confirm a high level of positioning accuracy and the added benefit of Kalman filtering stabilization.
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- Award ID(s):
- 1757207
- PAR ID:
- 10418738
- Date Published:
- Journal Name:
- Cluster Computing
- ISSN:
- 1386-7857
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.1007/s10586-022-03884-w
