Abstract

Terrain aided positioning (TAP) is a kind ofpositioning method which acquires position informationfrom the terrain elevation datum underneath the vehicle.This method has the characteristics of autonomy, allweather,anti-interference, strong stealthiness and highaccuracy. It is widely used in the navigation system forvarious aircrafts, cruise missiles and underwater vehicles.The fundamentals of TAP is that it firstly measures theterrain elevation underneath the vehicle using relevantsensors, then compares these datum with the referencedDigital Elevation Map (DEM) and acquires the positioninformation through matching algorithm. The system modelfor TAP currently used totally depends on the referencedDEM and the position acquired is the position referenced tothe map rather than the true position. Due to the DEMerror which is introduced during production procedure, theposition on the map is not the real position. In order toovercome the problem, the paper proposes an improvedTAP model which introduces the map error into the systemmodel and gets the recursive solution based on the Bayesianframework which is numerically solved by RPF particlefilter. From the simulation results, the new model hasextraordinary performance for handling the error of DEMand the algorithm can estimate the map error and acquirethe accurate position.