A Probabilistic Measurement Method For Terrain Aided Navigation for Airborne Platforms
Görkem Kandemir and Haluk Erdem Bingöl, Defense Industries Research and Development Institute of The Scientific and Technological Research Council of Turkey
Inertial Navigation Systems (INS) has an unbounded type of error characteristics. In order to overcome this problem, aiding systems are used to fuse information obtained via additional sensors with the INS solutions. Terrain Aided Navigation (TAN) is an integrated solution method which exploits Digital Terrain Elevation Maps (DTEM) and on board altimeter data to aid INS. In TAN applications, a position measurement is formed by comparing the terrain profiles, which are obtained by using on board altimeters, with a preloaded DTEM. In general, this position measurement is formed by finding the DTEM points which create minimum-mean-square error with the terrain profile that is measured with on board altimeters. Error on the formed position measurement varies with the terrain profile over which the airborne platform flies. This position measurement is used to keep INS errors bounded with various sensor fusion techniques, two of which are Extended Kalman Filtering (EKF) and Gaussian Sum Filtering (GSF). However, error on the position measurement may cause these techniques to produce very erroneous navigation data or to behave divergent. Probabilistic Measurement Method (PMM) offered in this paper results with more probable position measurements which have less errors. PMM method is used with an EKF structure and takes advantage of position error covariance that are readily available within the EKF. Joint Probability Density Function (JPDF) of horizontal error components are formed and used while the terrain profile is compared with DTEM in order to form a position measurement. In this paper, performances of conventional EKF, GSF, and EKF with PMM are investigated and evaluated for 10 different flight paths. Flight paths are chosen to have different terrain profiles and the performance evaluation is realized by comparing the mean horizontal and vertical position errors.