Study Of Motion Compensation For High Resolution ISAR Imaging

Inverse Synthetic Aperture Radar (ISAR) is mainly used for imaging and recognition of moving targets, like aircrafts, ships, missiles, and space debris. Recently, with the increasing requirement of image quality in the areas of defense and civilian applications, high-resolution ISAR imaging theory experiences a great development. However, we still have to face the new problems and challenges: How to develop a more realistic signal model? How to extract and employ fully motion characters? How to design more effective methods for motion compensation in high resolution ISAR imaging? Therefore, in this dissertation, we mainly focus on study of motion compensation for high resolution ISAR imaging. This dissertation is organized as follows:The first chapter is mainly associated with the historical development and direction of high resolution ISAR imaging radars, also a brief introduction on the work mode is made.The second chapter introduces the principle of ISAR imaging of moving targets and spinning targets.The third chapter proposes a novel algorithm for ISAR autofocus. This scheme, based on the properties of ISAR virtual array, employs the weighted signal subspace to express the phase errors left in the echoes after range-bin alignment and estimates the optimal weights sequentially via an optimization algorithm based on an entropy minimization principle, and its robustness and convergence can be ensured by the optimization method. Both the theoretical analysis and processing results of the real ISAR data have confirmed the feasibility of this new scheme.The fourth chapter introduces firstly a new signal model according to the motion character of spinning targets, which is different from the existing turntable model. According to this motion model, a novel algorithm for motion compensation for ISAR imaging of spinning targets is proposed. This method utilizes the generalized Radon transform (GRT) to detect the disordered sinusoidal envelopes of spinning scatterers, and then estimates the motion error by minimizing entropy of the reconstructed image. Finally, both theoretical analysis and simulation study confirm its robustness and effectiveness.The final chapter concludes the whole dissertation and points out the problems left for further research.