Localization of Airborne Platform Using Digital Elevation Model With Adaptive Weighting Inspired by Information Theory

Abstract

In this paper, we discuss the use of radar sensor to localize an airborne platform using a proposed rotation-invariant template matching algorithm based on data fusion. If a digital elevation model (DEM) under the platform is measured by radar, it can be used to estimate the position of the platform through comparing a pre-recorded DEM of large area through template matching. We implemented four different rotation-invariant template-matching algorithms and compared their performances from the perspective of position estimation accuracy and computational complexity. Based on the investigations, we chose a Fourier transform-based method that utilizes the characteristic of magnitude invariance with the shift of the original data. To improve the position estimation by providing more information, the gradients of DEM were derived. To combine the total error cost from the original DEM and its gradient, we employ an adaptive weighting inspired by the information theory. A higher weight is given to data that have more information. The information is measured as entropy, which is the inverse of the probability that the error cost is the minimum out of all possible error costs in the template-matching process. Through the suggested adaptive weighting, the position estimation accuracy has been increased by 16.04% compared with the uniform weighting. Compared with only using a DEM, the use of gradients of DEM improves the accuracy by 55.37%.