GPS Localization Accuracy Classification: A Context-Based Approach

Drawil, Nabil; Amar, Haitham M.; Basir, Otman

doi:10.1109/tits.2012.2213815

Cited by 166 publications

(93 citation statements)

References 29 publications

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“…The reflected signals, also well-known NLOS, will continuously affect the signal acquisition and tracking processes [38]. Thus, the velocity of the receiver must be used as indication to separate the model into dynamic and static cases [39].…”

Section: A Vehicle On-under Viaduct Determination Modelmentioning

confidence: 99%

Intelligent Viaduct Recognition and Driving Altitude Determination Using GPS Data

Hsu

Kamijo

2017

IEEE Trans. Intell. Veh.

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Abstract-The rapid development of road networks in highly urbanized cities requires a substantial number of viaducts to reduce the increasing traffic burden on urban highways. Unfortunately, this road design deteriorates the performance of global positioning system (GPS) navigators due to the signal blockage between satellite and receiver. As a result, it is difficult for GPS navigators to determine whether the vehicle is driving on the street or viaduct. Misleading information could confuse drivers and lead them to drive irregularly, which is dangerous in heavy traffic. This paper proposes a novel classification algorithm based on the fact that different satellite signals and conditions can be observed in the on-street and on-viaduct cases by the implementation of dynamic Bayesian network (DBN) to distinguish the driving area of a vehicle. In addition, the proposed DBN can also accurately estimate the driving altitude of the vehicle according to the experiment results.

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Section: A Vehicle On-under Viaduct Determination Modelmentioning

confidence: 99%

Intelligent Viaduct Recognition and Driving Altitude Determination Using GPS Data

Hsu

Kamijo

2017

IEEE Trans. Intell. Veh.

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“…We can get the precise location data of persons when they pass through ticket gates or buy items in shops. However, the GPS localization error ranges from a few meters to over 80 m [5], and the error ranges over 500 m [22] with Wi-Fi-based localization. As mobile phones are battery-powered, mobile phone users usually obtain the location at a very low frequency.…”

Section: B Validity Of Our Scenariosmentioning

confidence: 99%

“…Even if we use GPS localization, the error range can be over 80 m in urban canyons [5]. Moreover, there are many other methods for estimating the user's location since the GPS function consumes a lot of energy.…”

Section: Introductionmentioning

confidence: 99%

Location Anonymization With Considering Errors and Existence Probability

Sei

Ohsuga

2017

IEEE Trans. Syst. Man Cybern, Syst.

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Mobile devices that can sense their location using GPS or Wi-Fi have become extremely popular. However, many users hesitate to provide their accurate location information to unreliable third parties if it means that their identities or sensitive attribute values will be disclosed by doing so. Many approaches for anonymization, such as k-anonymity, have been proposed to tackle this issue. Existing studies for k-anonymity usually anonymize each user's location so that the anonymized area contains k or more users. Existing studies, however, do not consider location errors and the probability that each user actually exists at the anonymized area. As a result, a specific user might be identified by untrusted third parties. We propose novel privacy and utility metrics that can treat the location and an efficient algorithm to anonymize the information associated with users' locations. This is the first work that anonymizes location while considering location errors and the probability that each user is actually present at the anonymized area. By means of simulations, we have proven that our proposed method can reduce the risk of the user's attributes being identified while maintaining the utility of the anonymized data.

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“…Device localization approaches may use various sensors embedded in smartphones such as GPS, camera and microphone [4]- [6]. While GPS can directly provide physical locations, the accuracy may be unsatisfactory [4].…”

Section: Introductionmentioning

confidence: 99%

“…While GPS can directly provide physical locations, the accuracy may be unsatisfactory [4]. The distance between smartphones can be calculated via image processing [5].…”

Section: Introductionmentioning

confidence: 99%

Audio Fingerprinting for Multi-Device Self-Localization

Hon

Wang

Reiss

et al. 2015

IEEE/ACM Trans. Audio Speech Lang. Process.

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We investigate the self-localization problem of an ad-hoc network of randomly distributed and independent devices in an open-space environment with low reverberation but heavy noise (e.g. smartphones recording videos of an outdoor event). Assuming a sufficient number of sound sources, we estimate the distance between a pair of devices from the extreme (minimum and maximum) time difference of arrivals (TDOAs) from the sources to the pair of devices without knowing the time offset. The obtained inter-device distances are then exploited to derive the geometrical configuration of the network. In particular, we propose a robust audio fingerprinting algorithm for noisy recordings and perform landmark matching to construct a histogram of the TDOAs of multiple sources. The extreme TDOAs can be estimated from this histogram. By using audio fingerprinting features, the proposed algorithm works robustly in very noisy environments. Experiments with free-field simulation and open-space recordings prove the effectiveness of the proposed algorithm.Index Terms-Ad-hoc microphone array, audio fingerprinting, multi-source, self-localization, time difference of arrival (TDOA) estimation.

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GPS Localization Accuracy Classification: A Context-Based Approach

Cited by 166 publications

References 29 publications

Intelligent Viaduct Recognition and Driving Altitude Determination Using GPS Data

Intelligent Viaduct Recognition and Driving Altitude Determination Using GPS Data

Location Anonymization With Considering Errors and Existence Probability

Audio Fingerprinting for Multi-Device Self-Localization

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