Localization Convolutional Neural Networks Using Angle of Arrival Images

Location services, fundamentally, rely on two components: a mapping system and a positioning system. The mapping system provides the physical map of the space, and the positioning system identifies the position within the map. Outdoor location services have thrived over the last couple of decades because of well-established platforms for both these components (e.g. Google Maps for mapping, and GPS for positioning). In contrast, indoor location services haven't caught up because of the lack of reliable mapping and positioning frameworks. Wi-Fi positioning lacks maps and is also prone to environmental errors. In this paper, we present DLoc, a Deep Learning based wireless localization algorithm that can overcome traditional limitations of RF-based localization approaches (like multipath, occlusions, etc.). We augment DLoc with an automated mapping platform, MapFind. MapFind constructs location-tagged maps of the environment and generates training data for DLoc. Together, they allow off-the-shelf Wi-Fi devices like smartphones to access a map of the environment and to estimate their position with respect to that map. During our evaluation, MapFind has collected location estimates of over 105 thousand points under 8 different scenarios with varying furniture positions and people motion across two different spaces covering 2000 sq. Ft. DLoc outperforms stateof-the-art methods in Wi-Fi-based localization by 80% (median & 90 th percentile) across the two different spaces. CCS CONCEPTS • Networks → Location based services; • Computing methodologies → Robotic planning; Supervised learning; • Information systems → Sensor networks.

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“…There has been some recent work in localization using deep neural networks [5,13,43,62,75]. We differ from this work along four axes.…”

Section: Related Workmentioning

confidence: 93%

Deep learning based wireless localization for indoor navigation

Ayyalasomayajula

Arun

et al. 2020

Proceedings of the 26th Annual International Conference on Mobile Computing and Networking

138

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“…Different propagation environments lead to different patterns in D m thereby suggesting to process each corresponding Φ m as a single-channel picture. In such regard, we employ a 2D CNN whose usage is well established in indoor localization and have been shown to be capable of achieving sub-meter accuracy with custom Ultra Wide Band (UWB) signals in indoor scenarios [10].…”

Section: Data Preprocessingmentioning

confidence: 99%

“…Along these lines, [45]- [47] tackle the localization accuracy in harsh environments, showing that CNNs are able to mitigate NLoS effects and obtain sub-meter accuracy with Ultra Wide Band (UWB) signals. Even though Channel State Information (CSI) and Angle-of-Arrival (AoA) measurements can be leveraged to perform direct localization [10], CNNs have been used to forecast channel information [48], which could be fed to any system based on the trilateration technique. Differently from the above-mentioned literature that exploits several anchor nodes, SARDO makes use a single anchor and achieves time and spatial diversity by moving the UAV along controlled trajectories.…”

Section: Related Workmentioning

confidence: 99%

“…In addition, RRC serves as transport protocol for Non-Access Stratum (NAS) signaling messages between a UE and its Mobile Management Entity (MME).We introduce specific changes to the srsLTE software but keep our system in full compliance with 3GPP standard guidelines. We update the NAS signaling10 for the Tracking Area Update (TAU) Procedure so that the UE reveals its International Mobile Subscriber Identity (IMSI) while exchanging messages with the base station, i.e., our UAV cell. In this way, it is straightforward to identify the UE and start the localization process within a short disruption time window.…”

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confidence: 99%

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SARDO: An Automated Search-and-Rescue Drone-Based Solution for Victims Localization

Albanese¹,

Sciancalepore²,

Costa‐Pérez

2022

IEEE Trans. on Mobile Comput.

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Natural disasters affect millions of people every year. Finding missing persons in the shortest possible time is of crucial importance to reduce the death toll. This task is especially challenging when victims are sparsely distributed in large and/or difficult-to-reach areas and cellular networks are down. In this paper we present SARDO, a drone-based search and rescue solution that leverages the high penetration rate of mobile phones in the society to localize missing people. SARDO is an autonomous, all-in-one drone-based mobile network solution that does not require infrastructure support or mobile phones modifications. It builds on novel concepts such as pseudo-trilateration combined with machine-learning techniques to efficiently locate mobile phones in a given area. Our results, with a prototype implementation in a field-trial [1], show that SARDO rapidly determines the location of mobile phones (∼ 3 min/UE) in a given area with an accuracy of few tens of meters and at a low battery consumption cost (∼ 5%). State-of-the-art localization solutions for disaster scenarios rely either on mobile infrastructure support or exploit onboard cameras for human/computer vision, IR, thermal-based localization. To the best of our knowledge, SARDO is the first drone-based cellular search-and-rescue solution able to accurately localize missing victims through mobile phones.

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“…Using different antenna arrays a structured pair of neural networks is used to estimate the antenna beam. However, although they use AoA measurements in a time-series manner to train a CNN for position estimation in [ 69 ] they do not estimate the position within a ToA-setup. Xiao et al [ 70 ] propose denoising autoencoders to model the noise of reference locations.…”

Section: Related Workmentioning

confidence: 99%

A Deep Learning Approach to Position Estimation from Channel Impulse Responses

Niitsoo

Edelhäußer

Eberlein

et al. 2019

Sensors

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Radio-based locating systems allow for a robust and continuous tracking in industrial environments and are a key enabler for the digitalization of processes in many areas such as production, manufacturing, and warehouse management. Time difference of arrival (TDoA) systems estimate the time-of-flight (ToF) of radio burst signals with a set of synchronized antennas from which they trilaterate accurate position estimates of mobile tags. However, in industrial environments where multipath propagation is predominant it is difficult to extract the correct ToF of the signal. This article shows how deep learning (DL) can be used to estimate the position of mobile objects directly from the raw channel impulse responses (CIR) extracted at the receivers. Our experiments show that our DL-based position estimation not only works well under harsh multipath propagation but also outperforms state-of-the-art approaches in line-of-sight situations.

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Localization Convolutional Neural Networks Using Angle of Arrival Images

Cited by 15 publications

References 7 publications

Deep learning based wireless localization for indoor navigation

Deep learning based wireless localization for indoor navigation

SARDO: An Automated Search-and-Rescue Drone-Based Solution for Victims Localization

A Deep Learning Approach to Position Estimation from Channel Impulse Responses

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