A GPS sensor is widely used in many areas such as navigation, or air traffic control. Particularly, the car navigation system is equipped with GPS sensor for locational information. However, when a car goes through a tunnel, forest, or built-up area, GPS receiver cannot get the enough number of satellite signals. In these situations, a GPS receiver does not reliably work. A GPS error can be formulated by sum of bias error and sensor noise. The bias error is generated by the geometric arrangement of satellites and sensor noise error is generated by the corrupted signal noise of receiver. To enhance GPS sensor accuracy, these two kinds of errors have to be removed. In this research, we make the road database which includes Road Database File (RDF). RDF includes road information such as road connection, road condition, coordinates of roads, lanes, and stop lines. Among the information, we use the stop line coordinates as a feature point to correct the GPS bias error. If the relative distance and angle of a stop line from a car are detected and the detected stop line can be associated with one of the stop lines in the database, we can measure the bias error and correct the car’s location. To remove the other GPS error, sensor noise, the Kalman filter algorithm is used. Additionally, using the RDF, we can get the information of the road where the car belongs. It can be used to help the GPS correction algorithm or to give useful information to users.

본 연구에서는 해안 도시 하천의 범람으로 인한 홍수 재해 발생시 예상될 수 있는 피해에 대해 적절한 홍수예경보 및 피난대책을 수립하고자 대표적인 해안 도시 하천의 특성을 가지는 부산시 온천천 유역을 대상으로 수치지도에서 각종 지형자료를 추출하였고 수문 GIS 자료를 구축하였다. 강우 분석은 강우의 공간적 특성을 대상유역인 온천천에 티센망을 이용하여 고려하였으며 강우의 시간적 분포는 Huff의 2분위, 6차 회귀다항식을 이용하여 분석하였다. 홍수예경보 발