Computational Algorithms for Improved Synthetic Aperture Radar Image Focusing
Research output: Book/Report › Doctoral thesis › Collection of Articles
|Publisher||Tampere University of Technology|
|Number of pages||65|
|Publication status||Published - 26 Oct 2018|
|Publication type||G5 Doctoral dissertation (article)|
|Name||Tampere University of Technology. Publication|
In SAR and ISAR imaging, the image reconstruction process is based on using the relative motion between the radar and the scene. An important part of the signal processing chain is the estimation and compensation of this relative motion. The increased spatial resolution and number of receive channels cause the approximations used to derive conventional algorithms for image reconstruction and motion compensation to break down. This leads to limited applicability and performance limitations in non-ideal operating conditions.
This thesis presents novel research in the areas of data-driven motion compensation and image reconstruction in non-cooperative ISAR and Multichannel Synthetic Aperture Radar (MSAR) imaging. To overcome the limitations of conventional algorithms, this thesis proposes novel algorithms leading to increased estimation performance and image quality. Because a real-time imaging capability is important in many applications, special emphasis is placed on the computational aspects of the algorithms.
For non-cooperative ISAR imaging, the thesis proposes improvements to the range alignment, time window selection, autofocus, time-frequency-based image reconstruction and cross-range scaling procedures. These algorithms are combined into a computationally eﬃcient non-cooperative ISAR imaging algorithm based on mathematical optimization. The improvements are experimentally validated to reduce the computational burden and signiﬁcantly increase the image quality under complex target motion dynamics.
Time domain algorithms oﬀer a non-approximated and general way for image reconstruction in both ISAR and MSAR. Previously, their use has been limited by the available computing power. In this thesis, a contrast optimization approach for time domain ISAR imaging is proposed. The algorithm is demonstrated to produce improved imaging performance under the most challenging motion compensation scenarios. The thesis also presents fast time domain algorithms for MSAR. Numerical simulations conﬁrm that the proposed algorithms oﬀer a reasonable compromise between computational speed and image quality metrics.