Peer-to-Peer Localization in Urban and Indoor Environments

Chen, Siwen; Tan, Siyu; Seow, Chee Kiat

doi:10.2528/pierb11070701

Cited by 5 publications

(4 citation statements)

References 42 publications

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“…1: Area of Possible MD location using a LOS path [9] B. Construction of LPMD from a NLOS path After constructing the APMD for the LOS path, we will make use of a NLOS path to construct the LPMD [10], [12]. can be found at MD B .…”

Section: A Construction Of Apmd From a Los Pathmentioning

confidence: 99%

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Single reference device-based weighted least squares localization using LOS and NLOS paths

Chen

Seow

Tan

2015

2015 10th International Conference on Information, Communications and Signal Processing (ICICS)

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This paper proposes a novel localization scheme to estimate the location of the mobile device by using single reference device. This scheme makes use of the bidirectional information of the line of sight (LOS) and non line of sight (NLOS) paths to construct the area of possible mobile device (APMD) and line of possible mobile device (LPMD) respectively. The location of the mobile device is determined by using the weighted least squares solution based on the intersection of these APMD and LPMD. Simulation and experimental results demonstrate that the performance of our proposed localization scheme outperforms the existing localization schemes by significant margins.

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Section: A Construction Of Apmd From a Los Pathmentioning

confidence: 99%

“…However, [9] only can work well when the NLOS path is not dominant as compared to the LOS path. In the severe multipath environment, the NLOS paths can be comparable to the LOS paths [10]- [12]. Hence the accuracy in [9] will be degraded.…”

Section: Introductionmentioning

confidence: 99%

Single reference device-based weighted least squares localization using LOS and NLOS paths

Chen

Seow

Tan

2015

2015 10th International Conference on Information, Communications and Signal Processing (ICICS)

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“…I NDOOR localization and tracking algorithms for wireless devices have been widely studied. The frameworks of these algorithms are based on measurements such as received signal strength (RSS) and time-of-arrival (TOA) [1]- [3]. RSS is easy to measure using wireless devices, but struggles to achieve high-ranging accuracy due to severe multipath overlapping in indoor environments [1].…”

Section: Introductionmentioning

confidence: 99%

TOA-Based Indoor Localization and Tracking With Inaccurate Floor Plan Map via MRMSC-PHD Filter

2019

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This paper proposes a novel indoor localization scheme to jointly track a mobile device (MD) and update an inaccurate floor plan map using the time-of-arrival measured at multiple reference devices (RDs). By modeling the floor plan map as a collection of map features, the map and MD position can be jointly estimated via a multi-RD single-cluster probability hypothesis density (MSC-PHD) filter. Conventional MSC-PHD filters assume that each map feature generates at most one measurement for each RD. If single reflections of the detected signal are considered as measurements generated by map features, then higher-order reflections, which also carry information on the MD and map features, must be treated as clutter. The proposed scheme incorporates multiple reflections by treating them as virtual single reflections reflected from inaccurate map features and traces them to the corresponding virtual RDs (VRDs), referred to as a multi-reflection-incorporating MSC-PHD (MRMSC-PHD) filter. The complexity of using multiple reflection paths arises from the inaccuracy of the VRD location due to inaccuracy in the map features. Numerical results show that these multiple reflection paths can be modeled statistically as a Gaussian distribution. A computationally tractable implementation combining a new greedy partitioning scheme and a particle-Gaussian mixture filter is presented. A novel mapping error metric is then proposed to evaluate the estimated map?s accuracy for plane surfaces. Simulation and experimental results show that our proposed MRMSC-PHD filter outperforms existing MSC-PHD filters by up to 95% in terms of average localization and by up to 90% in terms of mapping accuracy.

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“…• Based on di↵erent localization methodology [6,7,8,9], detection of the drone can be e↵ective but inaccurate in estimating the drone location.…”

Section: Introductionmentioning

confidence: 99%

RF and Network Signature-based Machine Learning on Detection of Wireless Controlled Drone

Teoh

Seow

2019

2019 PhotonIcs &Amp; Electromagnetics Research Symposium - Spring (PIERS-Spring)

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Over the years, drone usage have become an increasing part of the ever-connected society that we are currently living in. Its usages have proliferated beyond the military sector to various commercial and consumer activities such as package delivery, disaster relief, agriculture and filming. Wi-Fi controlled drone has increased its popularity for personal use due to its a↵ordability, and the ease of operating the drone through smart-devices like mobile phone, tablets and computers. As such, this increases the likelihood of drone presence in various environments, especially in critical government infrastructure, leading to various privacy and security concern by the authorities and the public with malicious intent. Therefore, various signature-based methodology of drone detection has emerged such as the visual and Radio Frequency (RF) signature-based detection method. Visual signature-based detection relies on camera capture and image processing but this is an expensive approach. Whereas, RF signature-based detection relies on the identification of the emission of RF signal by the drone. However, since most commercial electronics devices were built based on Wi-Fi technology, the di↵erentiation of the RF signals transmitted between a drone or a standard Wi-Fi device in a crowded Wi-Fi environment such as a school campus or city area is a challenging task. In this paper, we propose a novel Machine Learning (ML) approach that leverages on both RF and network packets measurement to identify the presence of Wi-Fi drone in an urban setting. These two measurements were jointly analyzed to create unique signatures to di↵erentiate a Wi-Fi drone and a standard Wi-Fi device. Furthermore, we also propose a meticulous pre-processing procedure and a better training scheme of using Stratified K-Fold Cross-Validation (SKFCV), to enhance the richness in the data signature and fully exploit the permutation of the data during training respectively for better performance of the ML models. Two supervised classification ML models, namely the Logistic Regression (LR), and Artificial Neural Network (ANN) were applied using the joint data measurements to identify the presence of drone in dense Wi-Fi environment. The experimental results have shown that the proposed novel ML approach of using both RF and network measurement signatures coupled with the pre-processing and training methodology on LR and ANN ML models have outperformed the traditional RF signature-based drone detection ML accuracy results by 15.1% and 21.63% respectively in a crowded Wi-Fi environment.

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Peer-to-Peer Localization in Urban and Indoor Environments

Cited by 5 publications

References 42 publications

Single reference device-based weighted least squares localization using LOS and NLOS paths

Single reference device-based weighted least squares localization using LOS and NLOS paths

TOA-Based Indoor Localization and Tracking With Inaccurate Floor Plan Map via MRMSC-PHD Filter

RF and Network Signature-based Machine Learning on Detection of Wireless Controlled Drone

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