Radial basis function network-based available measurement classification of interferometric radar altimeter for terrain-aided navigation

Abstract

The purpose of this study is to propose a measurement classification method necessary to implement precision terrain-aided navigation (TAN) by using an interferometric radar altimeter (IRA) as a technology that can replace global positioning system/inertial navigation system integrated navigation. IRA is a sensor that extracts the angle perpendicular to the direction of flight, the look angle, and the slant range from the aircraft to the nearest terrain point. Unlike the radio altimeter which only measures the direct downward distance, IRA can be converted into three-dimensional coordinates in the navigation system. However, the IRA output has a disadvantage that it has uncertainty that cannot be predicted due to the signal processing and environmental factors. Therefore, a useful navigation technique for classifying sensor outputs is needed to implement precision TAN. This study introduces the radial basis function network and extreme learning machine methods to classify available IRA measurements and verifies the suitability of the proposed classification method by applying it to the bank of Kalman filter) and particle filter-based TAN.