Decision fusion for robust horizon estimation using Dempster Shafer Combination Rule

Abstract

In this paper, we address the problem of decision fusion for robust horizon estimation using Dempster Shafer Combination Rule (DSCR). We provide a framework for decision fusion to select robust horizon estimate out of `n' estimates, based on confidence factor. Vision-based attitude estimation depends on robust horizon estimation and no single algorithm gives accurate results for different kind of scenarios. We propose to combine the evidence parameters to generate confidence factor using DSCR to justify the correctness of the estimated horizon. We compute Confidence Interval (CI) based on Gaussian Mixture Model (GMM). We also propose two techniques to provide evidence parameters for the estimated horizon using CI. We demonstrate the effectiveness of the decision framework on clear and noisy data sets of simulated and real images/videos captured by Micro Air Vehicle (MAV).