Imaging for a Forward Scanning Automotive Synthetic Aperture Radar

In this paper, we propose a forward scanning synthetic aperture radar methodology for a forward-looking automotive (low-terahertz) radar which combines scene scanning with synthetic aperture processing, resulting in enhanced angular resolution and improved imaging. We propose two algorithms: i) a modified back-projection algorithm, and ii) a compressed sensing based back-projection algorithm. We suggest techniques to reduce computational complexity of the proposed algorithms. Results of simulation and real-data experiments corroborate the validity of our proposed methodology and algorithms. The data (real-data) contains three files. The two .mat files contain data and the .m file is the matlab code. Running the matlab code by loading one of the data files generates a basic radar image. The paper basically uses this data to test our proposed methods.Gishkori, Shahzad; Daniel, Liam; Gashinova, Marina; Mulgrew, Bernard. (2018). Imaging for a Forward Scanning Automotive Synthetic Aperture Radar, [dataset]. The University of Edinburgh. The School of Engineering. Institute for Digital Communications. https://doi.org/10.7488/ds/2440

Imaging for a Forward Scanning Automotive Synthetic Aperture Radar

In this paper, we propose a forward scanning synthetic aperture radar methodology for a forward-looking automotive (low-terahertz) radar, which combines scene scanning with synthetic aperture processing, resulting in enhanced angular resolution and improved imaging. We propose two algorithms, first, a modified back-projection (BP) algorithm and, second, a compressed sensing-based BP algorithm. We suggest techniques to reduce computational complexity of the proposed algorithms. Results of simulation and real-data experiments corroborate the validity of our proposed methodology and algorithms.</p