MobIntel: Passive Outdoor Localization via RSSI and Machine Learning

Bao, Fanchen; Mazokha, Stepan; Hallstrom, Jason O.

doi:10.1109/wimob52687.2021.9606338

Cited by 9 publications

(5 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, we chose this direction as it can provide a double benefit of generating REMs and a pose estimate for robot localization purposes. The accuracy can be further improved if more measurements are performed in the application area as in fingerprinting [61], which can be a laborious effort for early deployments of robots outdoors and online learning-based approaches may assist towards this direction by inferring patterns within the measurement variation over time [84].…”

Section: Lessons Learnedmentioning

confidence: 99%

NR5G-SAM: A SLAM Framework for Field Robot Applications Based on 5G New Radio

Karfakis

Couceiro

Portugal

2023

Sensors

View full text Add to dashboard Cite

Robot localization is a crucial task in robotic systems and is a pre-requisite for navigation. In outdoor environments, Global Navigation Satellite Systems (GNSS) have aided towards this direction, alongside laser and visual sensing. Despite their application in the field, GNSS suffers from limited availability in dense urban and rural environments. Light Detection and Ranging (LiDAR), inertial and visual methods are also prone to drift and can be susceptible to outliers due to environmental changes and illumination conditions. In this work, we propose a cellular Simultaneous Localization and Mapping (SLAM) framework based on 5G New Radio (NR) signals and inertial measurements for mobile robot localization with several gNodeB stations. The method outputs the pose of the robot along with a radio signal map based on the Received Signal Strength Indicator (RSSI) measurements for correction purposes. We then perform benchmarking against LiDAR-Inertial Odometry Smoothing and Mapping (LIO-SAM), a state-of-the-art LiDAR SLAM method, comparing performance via a simulator ground truth reference. Two experimental setups are presented and discussed using the sub-6 GHz and mmWave frequency bands for communication, while the transmission is based on down-link (DL) signals. Our results show that 5G positioning can be utilized for radio SLAM, providing increased robustness in outdoor environments and demonstrating its potential to assist in robot localization, as an additional absolute source of information when LiDAR methods fail and GNSS data is unreliable.

show abstract

Section: Lessons Learnedmentioning

confidence: 99%

NR5G-SAM: A SLAM Framework for Field Robot Applications Based on 5G New Radio

Karfakis

Couceiro

Portugal

2023

Sensors

View full text Add to dashboard Cite

show abstract

“…In the indoor controlled environment, previous works attempt to localize users through RSSI-based distance measurements [6], [7]. On the other side, in outdoor scenarios, there has been an attempt to use machine learning techniques to lower the distance-estimation error [8]. But errors still go up 16m, that too in semi-controlled environments.…”

Section: Introductionmentioning

confidence: 99%

Do WiFi Probe-Requests Reveal Your Trajectory?

Mishra

Viana

Achir

2023

2023 IEEE Wireless Communications and Networking Conference (WCNC)

View full text Add to dashboard Cite

In this paper, we propose the first framework that introduces the concept of the user's bounded trajectory. We propose to leverage the signal strength of users' public WiFi probe requests collected from measurements of multiple deployed WiFi sniffers. First, we investigate and characterize errors in RSSI-based radial-distance (between the user and each sniffer) estimation. Then, we approximate such radial distances leverage and deduce bounds associated with a user's position. Finally, we infer a user's bounded trajectory using the spatiotemporal bounds of users' locations over time. We guarantee the bounds to enclose a user in space and time, with 95% confidence and a 10% margin of error. Using real-world and large-scale synthetic datasets under heterogeneous contexts and wireless conditions, we infer trajectories with bounds' width of less than 10m in 70% of cases with users' inclusiveness close to 100%.

show abstract

“…There has been continuous interest in WiFi localization over the past decade. This area holds importance to a variety of applications, including mobile navigation [5]- [7], mobility intelligence [8]- [10], and more. Advancements in the extraction of channel state information from common off-the-shelf devices have enabled higher accuracy localization methods that extract novel features from raw data [11]- [14].…”

Section: Introductionmentioning

confidence: 99%

MobLoc: CSI-Based Location Fingerprinting With MUSIC

Mazokha,

Bao,

Sklivanitis

et al. 2023

J. Ind. Sea. Pos. Nav.

Self Cite

View full text Add to dashboard Cite

Many CSI-based localization methods have been proposed over the last decade. Fingerprinting has been one of the highest-achieving approaches due to its capacity to capture environmental characteristics that are not readily captured using classic localization mechanisms such as multilateration. However, oftentimes the proposed methods are limited by reliance on large-scale training datasets. Further, methods are rarely evaluated on nonstationary devices, which are the most common in realworld environments.In our work, we address these challenges by introducing MobLoc. We adopt MUSIC pseudo-spectrum-based fingerprinting, which can benefit from, but does not heavily rely upon a large number of packets for each fingerprint. To evaluate our method, we leverage a publicly available dataset of passively collected CSI measurements, DLoc [1], where an emitter sends signals in motion. We also benchmark MobLoc against a series of state-of-the-art localization methods.The results demonstrate that our method outperforms SpotFi [2], EntLoc [3], and AngLoc [4] and falls very short of achieving DLoc accuracy. On the DLoc dataset, MobLoc achieves 0.33m median (and 0.82m, 90th percentile) localization error in a simple environment and 1.15m median (2.59m, 90th percentile) localization error in a complex environment. However, despite MobLoc not exceeding DLoc accuracy, we consider its performance as a trade-off for computational resources required to deploy the method in a real-world environment. We anticipate that this advantage will enable the adoption of MobLoc in city-scape localization systems, where the cost of computational resources is key.

show abstract

MobIntel: Passive Outdoor Localization via RSSI and Machine Learning

Cited by 9 publications

References 14 publications

NR5G-SAM: A SLAM Framework for Field Robot Applications Based on 5G New Radio

NR5G-SAM: A SLAM Framework for Field Robot Applications Based on 5G New Radio

Do WiFi Probe-Requests Reveal Your Trajectory?

MobLoc: CSI-Based Location Fingerprinting With MUSIC

Contact Info

Product

Resources

About