• Journal of Institute of Control, Robotics and Systems
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• v.22 no.9
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• pp.723-732
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• 2016

This paper proposes an approach to fused navigation of an unmanned surface vehicle(USV) and to detection of the outlier or interference of global positioning system(GPS). The method fuses available sensor measurements through extended Kalman filter(EKF) to find the location and attitude of the USV. The method uses error covariance of EKF for detection of GPS outlier or interference. When outlier or interference of the GPS is detected, the method excludes GPS data from navigation process. The measurements to be fused for the navigation are GPS, acceleration, angular rate, magnetic field, linear velocity, range and bearing to acoustic beacons. The method is tested through simulated data and measurement data produced through ground navigation. The results show that the method detects GPS outlier or interference as well as the GPS recovery, which frees navigation from the problem of GPS abnormality.

• 한국ITS학회:학술대회논문집
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• 2003.11a
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• pp.297-302
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• 2003

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.5D
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• pp.731-738
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• 2006

Facing congestion, people want to know traffic information about their routes, especially real-time link travel time (LTT). In this paper, as a sequel paper of the previous non-taxi based LTT generating study by Choi et al. (1998), taxi based GPS probes have been tried to produce LTT for urban arterials. Taxis in itself are good deployment mode of GPS probes although it by nature experiences boarding and alighting time noises which should be accounted. A heuristic real-time dynamic outlier filter algorithm for taxi GPS probe has been developed focusing on urban arterials. An actual traffic survey for dynamic link travel times has been conducted using license plate method for the test arterials of Seoul city transportation network. With the algorithm, it is estimated that 70% of outliers have been filtered and the relative error has been improved by 73.7%. The filtering algorithm developed here would be expected to be in use for other spatial sites with some calibration efforts. Some limitations and future research agenda have also been discussed.

• Smart Structures and Systems
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• v.20 no.2
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• pp.219-229
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• 2017

Outlier detection is an imperative task to identify the occurrence of abnormal events before the structures are suffered from sudden failure during their service lives. This paper proposes a two-phase method for the outlier detection of Global Positioning System (GPS) monitoring data. Prompt judgment of the occurrence of abnormal data is firstly carried out by use of the relational analysis as the relationship among the data obtained from the adjacent locations following a certain rule. Then, a negative selection algorithm (NSA) is adopted for further accurate localization of the abnormal data. To reduce the computation cost in the NSA, an improved scheme by integrating the adjustable radius into the training stage is designed and implemented. Numerical simulations and experimental verifications demonstrate that the proposed method is encouraging compared with the original method in the aspects of efficiency and reliability. This method is only based on the monitoring data without the requirement of the engineer expertise on the structural operational characteristics, which can be easily embedded in a software system for the continuous and reliable monitoring of civil infrastructure.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.2D
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• pp.319-327
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• 2009

In order to estimate accurate position by GPS observations, it is prerequisite to define both of the correct function model and the realistic stochastic model. In the case that un-modeled outliers exist in observations, estimates become biased, and their standard deviations are unable to be used as a measure which represents their accuracy. Hence, such outliers should be appropriately removed from the observations before estimating final solutions, so that the accuracy can be maximized with the improvement of the reliability. For this purpose, this research deals with quality control and quality measure computation algorithms for GPS stand-alone positioning. After theoretical studies, all the algorithms have been implemented and tested with real observations. Results of the tests indicate that the reliability of the estimated position is improved by increasing redundancy as well as using good satellite geometry and more realistic stochastic model. Moreover, the adaptation of the quality control procedure enable to improve positioning reliability and accuracy by appropriately excluding outlier in observations.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.7 no.6
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• pp.21-30
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• 2008

The travel speed collected by the prove-car equipped with the GPS has the problems, which are the data's stability and finding out the representative travel speed, by the influence of the traffic signal and etc. at the interrupted traffic. This study was conducted to develop the method of filtering the outlier data from the data collected by the prove-car. The method to remove the outlier data from the serial data which were collected by the prove-car was adapted to each of the quartile deviation statistics model and the management graphic statistics model. The rate of removing the outlier data by the quartile deviation method was $0{\sim}3.7%$ while the rate by the management graphic statistic methods was $0.3{\sim}7.2%$. Both methods show the low removal rate at the dawn time when the traffic is inactivity, on the other hand the remove rate is high during the daytime. However, both methods have the problem such that the threshold level for removing the outlier data was established at the low bound in the case as good as the statistics model. Therefore, it is required for the experience calibration.

• The Journal of Korea Robotics Society
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• v.16 no.4
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• pp.306-312
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• 2021

This paper describes localization of autonomous underwater vehicles(AUV), which can be used when some navigation sensor data are an outlier. In that situation, localization through existing navigation algorithms causes problems in long-range localization. Even if an outlier sensor data occurs once, problems of localization will continue. Also, if outlier sensor data is related to azimuth (direction of AUV), it causes bigger problems. Therefore, a parallel localization module, in which different algorithms are performed in a normal and abnormal situation should be designed. Before designing a parallel localization module, it is necessary to study an effective method in the abnormal situation. So, we propose a localization method through machine learning. For this method, a learning model consists of only Fully-Connected and trains through randomly contaminated real sea data. The ground truth of training is displacement between subsequent GPS data. As a result, average error in localization through the learning model is 0.4 times smaller than the average error in localization through the existing navigation algorithm. Through this result, we conclude that it is suitable for a component of the parallel localization module.

• Journal of Korean Society of Transportation
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• v.27 no.2
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• pp.47-56
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• 2009

Effective detection and correction of outliers of raw traffic data collected from the field is of keen interest because reliable traffic information is highly dependent on the quality of raw data. Global positioning system (GPS) based traffic surveillance systems are capable of producing individual vehicle speeds that are invaluable for various traffic management and information strategies. This study proposed a locally weighted regression (LWR) based filtering method for individual vehicle speed data. An important feature of this study was to propose a technique to generate synthetic outliers for more systematic evaluation of the proposed method. It was identified by performance evaluations that the proposed LWR-based method outperformed an exponential smoothing. The proposed method is expected to be effectively utilized for filtering out raw individual vehicle speed data.

• International Journal of Highway Engineering
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• v.13 no.2
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• pp.175-185
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• 2011

Geometric information is a key element for evaluating traffic safety and road maintenance. This study developed an algorithm to identify horizontal alignment using global positioning system(GPS) and inertial navigation system(INS) data. Roll and heading information extracted from GPS/INS were utilized to classify horizontal alignment into tangent, circular curve, and transition curve. The proposed algorithm consists of two components including smoothing for eliminating outlier and a heuristic classification algorithm. A genetic algorithm(GA) was adopted to calibrate parameters associated with the algorithm. Both freeway and rural highway data were used to evaluate the performance of the proposed algorithm. Promising results, which 90.48% and 88.24% of classification accuracy were obtainable for freeway and rural highway respectively, demonstrated the technical feasibility of the algorithm for the implementation.

• Journal of Astronomy and Space Sciences
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• v.19 no.2
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• pp.163-172
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• 2002

This article investigates the problem of data preprocessing for the precision orbit determination (POD) of low earth orbit satellite using GPS .aw data. Several data preprocessing algorithms have been developed to edit the GPS data automatically such that outlier deletion, cycle slip identification and correction, and time tag error correction. The GPS data are precisely edited for the accuracy of POD. Some methods of data preprocessing are restricted to the rate of the collections of the pseudorange and carrier phase measurements. This study considers the preprocessing efficiency varied with the rate, the quality of receiver and the altitude of the satellite's orbit. We also propose the proper methods in accordance with the rate for single frequency and dual frequency receivers.