• 전기학회논문지
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• 제63권9호
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• pp.1273-1280
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• 2014

This paper considers a GPS anti-spoofing problem. Spoofing is an intentional interference that mislead the GNSS receiver. The spoofing attack is very significant since the target receiver is not aware of being attacked from spoofing. Accelerometers can be used to detect the spoofing signal by being compared with the acceleration obtained from GPS information using Kalman filter. In this paper we propose an N by N-point average and M-point window algorithm to detect GPS spoofing by using accelerometers and GPS outputs. The performance of the proposed algorithm is analyzed using actual vehicle trajectory and spoofing trajectory generated from INS and GPS toolbox for simulation.

• 한국멀티미디어학회논문지
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• 제19권2호
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• pp.386-395
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• 2016

In trajectory-based navigation systems, a huge amount of trajectory data is needed for efficient route explorations. However, it would be very hard to collect trajectories from all the possible start and destination combinations. To provide a practical solution to this problem, we suggest a method combining collected GPS trajectories data into additional generated trajectories with new start and destination combinations without road information. We present a trajectory combination algorithm and its implementation with Scala programming language on Spark platform for big data processing. The experimental results proved that the proposed method can effectively populate the collected trajectories into valid trajectory paths more than three hundred times.

• Journal of Astronomy and Space Sciences
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• 제21권3호
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• pp.209-220
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• 2004

Some methods have been presented to get optimal formation trajectories in the step of configuration or reconfiguration, which subject to constraints of collision avoidance and final configuration. In this study, a method for optimal formation trajectory-planning is introduced in view of fuel/time minimization using parameter optimization technique which has not been applied to optimal trajectory-planning for satellite formation flying. New constraints of nonlinear equality are derived for final configuration and constraints of nonlinear inequality are used for collision avoidance. The final configuration constraints are that three or more satellites should be placed in an equilateral polygon of the circular horizontal plane orbit. Several examples are given to get optimal trajectories based on the parameter optimization problem which subjects to constraints of collision avoidance and final configuration. They show that the introduced method for trajectory-planning is well suited to trajectory design problems of formation flying missions.

• 한국컴퓨터정보학회:학술대회논문집
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• 한국컴퓨터정보학회 2022년도 제66차 하계학술대회논문집 30권2호
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• pp.89-92
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• 2022

본 논문에서는 딥러닝 모델 기반 보행자의 GPS 경로를 예측하는 시스템을 제안한다. 다양한 경로 예측 방식들 중 본 논문은 GPS 데이터 기반 경로 예측 연구이다. 시계열 데이터인 보행자의 GPS 경로를 학습하여 다음 경로를 예측하도록 하는 딥러닝 모델 기반 연구이다. 본 논문에서는 보행자의 GPS 경로를 딥러닝 모델이 학습할 수 있도록 데이터 구성 방식을 제시하였으며, 예측 범위에 큰 제약이 없는 예측 딥러닝 모델을 제안한다. 본 논문의 딥러닝 모델에 적합한 파라메터들을 제시하였으며, 우수한 예측 성능을 보이는 결과를 제시한다.

• 스마트미디어저널
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• 제5권4호
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• pp.26-40
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• 2016

최근 모바일 기기가 발전함에 따라 사용자의 위치 수집 및 분석 방법에 대한 연구가 활발히 이루어지고 있다. 사용자의 위치분석 방법 중 궤적 데이터 마이닝은 사용자의 궤적을 바탕으로 의미 있는 정보를 추출하기 위해 사용된다. 궤적 데이터 마이닝을 수행하기 위해서는 사용자의 GPS 궤적을 분석하여 Stay Point를 추출하는 과정이 선행되어야 한다. 기존의 Stay Point 추출 알고리즘은 두 가지의 임계값이 필요하며 해당 임계값들은 사용자가 임의로 설정함으로써 알고리즘의 신뢰도가 떨어지고, 실내에서 머문 지점과 실외에서 머문지점에 대한 구분이 없기 때문에 Stay Point 위치의 모호성이 높아지는 문제점이 있다. 본 논문에서는 통계적 분석방법을 이용하여 SP를 추출하는 방법을 제안한다. 제안 알고리즘은 가우시안 확률분포를 사용하여 실내와 실외 SP를 각각 추출함으로써 SP 위치의 모호성을 개선하였다. 또한, 사용자가 임의로 설정하는 임계값이 없기 때문에 알고리즘의 신뢰도를 향상시켰다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권2호
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• pp.600-619
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• 2015

We propose a cost-efficient hybrid algorithm for online location updates that efficiently combines feature point detection with the online trajectory-based sampling algorithm. Our algorithm is designed to minimize the average trajectory error with the minimal number of sample points. The algorithm is composed of 3 steps. First, we choose corner points from the map as sample points because they will most likely cause fewer trajectory errors. By employing the online trajectory sampling algorithm as the second step, our algorithm detects several missing and important sample points to prevent unwanted trajectory errors. The final step improves cost efficiency by eliminating redundant sample points on straight paths. We evaluate the proposed algorithm with real GPS trajectory data for various bus routes and compare our algorithm with the existing one. Simulation results show that our algorithm decreases the average trajectory error 28% compared to the existing one. In terms of cost efficiency, simulation results show that our algorithm is 29% more cost efficient than the existing one with real GPS trajectory data.

• Journal of Smart Tourism
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• 제1권2호
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• pp.19-25
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• 2021

Before COVID-19, tourist destinations have experienced problems with congestion of both famous tourist attractions and public transportation. Over-tourism is not an issue at this time, but it is likely to rekindle after the COVID-19 pandemic ends. One method of mitigating over-tourism is to estimate tourist behavior using a tourist transition model and consequently adjust public transportation operations. In this study, we propose a construction method for a model of tourist transitions among tourist attractions based on tourist GPS trajectory data. We construct tourist transition models using actual trajectory data for tourists staying in the vicinity of Kyoto City. The results verify the model performance.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2013년도 춘계학술발표대회
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• pp.472-473
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• 2013

The growth in number and efficiency of smart devices such as GPS enabled smart phones and PDAs present an unparalleled opportunity for diverse areas of life. However extraction of GPS traces for provision of services demand a huge storage space as well as computation overhead. This is a challenging task especially for the applications which provide runtime services. In this paper we provide a simplified model to extract GPS traces of moving objects at runtime. Road segment partitioning and measure of deviation in angle of trajectory path is incorporated to identify the significant data points. The number of these data points is minimized by our proposed approach in an efficient manner to overwhelm the storage and computation overhead. Further, the competent reconstruction of complete itinerary based on gathered data, is also ensured by proposed method.

• 항공우주기술
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• 제9권1호
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• pp.60-66
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• 2010

본 논문에서는 항공기 비행시험의 기준 비행 궤적을 획득하기 위해 수행된 GPS 수신기 데이터의 후처리에 대하여 다룬다. 항공기 비행시험은 나로우주센터의 추적 및 통신 장비의 성능을 검증하기 위해 나로호 탑재 시스템과 지상 장비들에 대해 수행된 통합 시험으로서 2007년부터 수차례 수행되었다. 항공기 시험에서 추적 및 항법 장비의 성능을 분석하기 위해서는 데이터 비교를 위한 참값인 기준 데이터가 반드시 필요하므로 본 논문에서는 이를 위해 성능이 검증된 이중주파수용 GPS 수신기인 NovAtel 사의 DL-V3 수신기를 운용하여 비행 데이터를 수집하고, 검증된 후처리 프로그램인 GrafNav로 후처리하여 수십 cm 오차 수준을 가지는 기준 궤적을 생성하였다.

• International Journal of Internet, Broadcasting and Communication
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• 제15권3호
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• pp.176-184
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• 2023

Points of interest (POIs) are widely used in tourism recommendations and to provide information about areas of interest. Currently, situation judgement using POI and GPS data is mainly rule-based. However, this approach has the limitation that inferences can only be made using predefined POI information. In this study, we propose an algorithm that uses POI data, GPS data, and schedule information to calculate the current speed, location, schedule matching, movement trajectory, and POI coverage, and uses machine learning to determine whether to stay or go. Based on the input data, the clustered information is labelled by k-means algorithm as unsupervised learning. This result is trained as the input vector of the SVM model to calculate the probability of moving and staying. Therefore, in this study, we implemented an algorithm that can adjust the schedule using the travel schedule, POI data, and GPS information. The results show that the algorithm does not rely on predefined information, but can make judgements using GPS data and POI data in real time, which is more flexible and reliable than traditional rule-based approaches. Therefore, this study can optimize tourism scheduling. Therefore, the stay detection algorithm using GPS movement trajectories and POIs developed in this study provides important information for tourism schedule planning and is expected to provide much value for tourism services.