Research on an Improved Terrain Aided Positioning Model

Terrain aided positioning (TAP) is a kind of positioning method which acquires position information from the terrain elevation datum underneath the vehicle. This method has the characteristics of autonomy, allweather, anti-interference, strong stealthiness and high accuracy. It is widely used in the navigation system for various aircrafts, cruise missiles and underwater vehicles. The fundamentals of TAP is that it firstly measures the terrain elevation underneath the vehicle using relevant sensors, then compares these datum with the referenced Digital Elevation Map (DEM) and acquires the position information through matching algorithm. The system model for TAP currently used totally depends on the referenced DEM and the position acquired is the position referenced to the map rather than the true position. Due to the DEM error which is introduced during production procedure, the position on the map is not the real position. In order to overcome the problem, the paper proposes an improved TAP model which introduces the map error into the system model and gets the recursive solution based on the Bayesian framework which is numerically solved by RPF particle filter. From the simulation results, the new model has extraordinary performance for handling the error of DEM and the algorithm can estimate the map error and acquire the accurate position.