In the Korean rail industry, tachometers and track circuits are used for the train navigation system. However, tachometers and track circuits cannot provide a high-accuracy navigation solution; however, this is essential in the rail industry. If a high-accuracy navigation system is adopted for rail track inspection cars, the maintenance cost of train service will be reduced. Generally, information gathered by various sensors is used to improve the accuracy of railway navigation systems. This paper proposes a sensor fusion algorithm in which INS/Tachometer/Doppler radar/DGPS/RFID/NHC sensors are combined. In addition, a map matching algorithm for high-speed trains is proposed.
The proposed INS/Tachometer/Doppler radar/DGPS/RFID/NHC sensors fusion algorithm called high accuracy navigation system based on multisensor information for high-speed train(HANST) is based on the federated Kalman filter. The federated Kalman filter consists of local filters and a master filter. In this paper, 6 local Kalman filters are used in the implementation of a federated Kalman filter. RFID local Kalman filters are specially designed for aperiodic measurement update. Moreover, RFID Kalman filters provide stability for whole navigation systems. Generally, the performance of federated Kalman filter-based navigation system depends on DGPS performance. If the DGPS signal is cut off, the performance of a federated Kalman filter-based navigation system decreases. However, if the proposed RFID filter is implemented, the accuracy of the whole navigation system remains stable in the DGPS signal cut off section.
A map matching algorithm for high-speed train navigation systems is also proposed. Generally, map matching algorithms are studied for car navigation. In car navigation, car map characteristics are used to improve the accuracy of map matching. For example, if a turn course is in the map and the navigation solution history has turn trace, the map matching algorithm matches the corner of the map and the corner of the navigation solution history. However, high-speed railways do not have sudden turn sections, so conventional map matching algorithms for car navigation are not appropriate for train navigation systems.
In this paper, an orthogonal projection map matching algorithm using a one-dimensional map is proposed. The computational load incurred due to map matching is reduced by the proposed map matching algorithm. In the proposed algorithm, the map matching range is determined using the velocity information obtained from the federated Kalman filter and a previous navigation solution. Therefore, map matching with a low computational load can be realized in the proposed map matching algorithm.
본 논문에서는 연합형 칼만필터를 기반으로 INS/Tachometer/Doppler radar/DGPS/ RFID 센서의 정보를 융합한 고속철도용 고정밀 항법 시스템을 제안 및 구현하였고, 추가적으로 지도정합 알고리즘을 적용하여 고속철도용 고정밀 항법 시스템인 HANST를 구현하였다.
제안된 RIFD 센서의 경우, 철도 선로 위만을 이동하는 철도시스템의 특성을 고려한 센서시스템이고 비주기적으로 RFID 정보가 들어오는 특성을 갖는다. 이러한 RFID 센서 시스템의 특성을 고려한 항법 필터를 구현하여 정확도를 향상시켰다.
또한, 지도정합 알고리즘을 제안하여 고속철도용 항법 시스템의 정밀도를 향상시켰다. 기존의 지도정합 알고리즘의 경우, 교차로나 회전하는 부분에서 항법 해의 변화를 지도와 정합하여 정확도를 향상시키는 방법으로 발전하였다. 하지만 철도시스템은 확연한 항법 해의 변화가 존재하는 교차나 회전 부분이 존재하지 않기 때문에 기존의 지도정합 방법은 적합하지 않다. 본 논문에서는 철도 차량은 철도 선로 위에서만 이동할 수 있다는 점에 착안하여 철도특성을 고려한 1차원 지도와 그 지도를 이용한 지도정합 방법을 제안하여 항법 시스템의 정확도를 향상시켰다.
