Model-based signal processing for radar imaging of targets with complex motions
Model-based signal processing for inverse synthetic aperture radar (ISAR) imaging of targets with complex motions is proposed in this dissertation. Target motion is the most important issue in radar imaging of an unknown target. Although widely recognized as a promising tool in target recognition, ISAR imaging is not yet fully operational in real-world data processing. This is mainly due to the fact that an unknown target, especially a non-cooperative target could have complex motions. First, the performance of existing motion compensation algorithms is evaluated. For this purpose, three sets of radar images of an aircraft, including blind motion compensated images, truth motion compensated images, and predicted images using electromagnetic-code simulation are generated. The limitations of existing radar imaging algorithms are identified after a comparison of the radar images. vii The remaining part of this research focuses on how to overcome these limitations. This is achieved by performing target feature extraction in the presence of complex motions, including three-dimensional (3D) motion, non-rigid body motion and high order motion. For a target with non-planar motion, an algorithm based on the phase analysis of multiple point scatterers is proposed to blindly detect the existence of 3D motion from radar data. An adaptive feature extraction technique is also applied for 3D ISAR image reconstruction from undersampled radar data when the target pose data is known. For a target with non-rigid body motions, adaptive chirplet signal representation is used to first separate signals from the main body and the rotating parts. Better extraction of target geometric features and micro-Doppler features are achieved after individual processing of the separated signal. For a target with high order motions, genetic algorithms are used to replace exhaustive search to reduce the computational time. Throughout the research, the use of physical models is emphasized for better understanding of the radar data. Model-based processing, including adaptive joint time-frequency techniques and genetic algorithms are applied in the information extraction process. Point scatterer simulations are extensively used to test the correctness and to demonstrate the concept of the proposed methods. Results from measurement data are included to demonstrate the effectiveness of the work on realworld problems.