Stochastic models and performance bounds for pose estimation using high-resolution radar data

摘要

Models for radar data have been pursued for many years. The classical work of Swerling and Marcum, and Gaussian and Rician models in general, have been most common. In contrast to these statistical models, there have been tremendous efforts expended to develop signature prediction code designed to predict radar returns from faceted objects. Ongoing research attempts to merge these efforts to yield good statistical models for radar data that are based in part on the outputs of signature prediction codes. Some of the issues are explored using simulated radar data from the University Research Initiative Synthetic Dataset. A general description of the class of Gaussian models for high resolution radar range profiles is given. These models include the possibility of having range profiles for different orientations that are correlated. The performance using these models for target orientation estimation and target recognition is described. A framework for analyzing the improvement in performance for using high resolution radar range profiles from multiple radar sensors, multiple polarizations, and multiple elevations is presented.