Comparison of Simple DME/DME Positioning Method and Augmentation Using Extended Kalman Filtering, Unscented Kalman Filtering and Particle Filtering

Modern air navigation is primarily based on FMS performance. Navigation with RNP and real time data processing, in areas with high air traffic density, would be very difficult without this type of system. These systems use selected types of navigation modes depending on the type of used signal sources. Selected FMSs and navigation modes apply filters that further improve the resulting position estimates. The most well-known type of the filter is Kalman Filter, which is widely implemented in GNSS receivers. At the same time, this type of filter is also used to create the resulting position based on a combination of two navigation sources, such as GNSS/INS. Another navigation method, among other also used for making a backup of the GNSS system, uses the DME ground beacon network and the FMS navigation method is called DME/DME. The DME/DME navigation is often not associated with selecting filter type to improve the resulting position estimate. This paper compares the designed simple DME/DME navigation method and the augmented ones, using Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), and less known Particle Filter (PF). The analyzed methods are evaluated for their resulting position estimate accuracy and for statistical indicators. The simulations are based on the data obtained from on-board Mi-17 helicopter recorders and are compared to the actual indicated GPS positions. Slant-range distances used in the simulations are obtained from the designed DME beacon network, which corresponds to the actual location of the beacons in the Czech Republic and neighboring countries.