Super-Resolution FMCW Radar System at 60 GHz for 3D Measurements

For the purpose of high-precision Radar Object Recognition (OR) system for real life emergency scenarios, a 60 GHz Super-Resolution FMCW radar imaging system is presented in this paper. Conventional radar imaging systems are limited by different hardware parameters such as bandwidth and antenna pattern resulting in distorted and noisy low-resolution (LR) images hindering the possibility of correct object recognition. Hence the radar imaging system proposed in this paper provides super-resolution (SR) images based on SR reconstruction methods typically used for low-cost optical components. Furthermore, the proposed SR radar system uses a low-cost single chip 60 GHz FMCW radar with two Rx antennas and one Tx antenna in a quasi monostatic configuration. The experimental validations are performed with geometrically complex targets by acquiring 3D radar images.

[1]  Shmuel Peleg,et al.  Image sequence enhancement using sub-pixel displacements , 1988, Proceedings CVPR '88: The Computer Society Conference on Computer Vision and Pattern Recognition.

[2]  Juan M. Lopez-Sanchez,et al.  3-D radar imaging using range migration techniques , 2000 .

[3]  Riccardo Lanari,et al.  Synthetic Aperture Radar Processing , 1999 .

[4]  LeRoy A. Gorham,et al.  SAR image formation toolbox for MATLAB , 2010, Defense + Commercial Sensing.

[5]  Moon Gi Kang,et al.  Super-resolution image reconstruction: a technical overview , 2003, IEEE Signal Process. Mag..

[6]  Thorsten Schultze,et al.  Object recognition radar system for partially reconstructed target image , 2017, 2017 European Radar Conference (EURAD).

[7]  R. Stolt MIGRATION BY FOURIER TRANSFORM , 1978 .

[8]  Thorsten Schultze,et al.  Super-resolution feature extraction imaging algorithm for complex objects , 2014, 2014 IEEE International Conference on Ultra-WideBand (ICUWB).

[9]  A. Yarovoy,et al.  Environmental imaging with a mobile UWB security robot for indoor localisation and positioning applications , 2013, 2013 European Microwave Conference.

[10]  F. Rocca,et al.  SAR data focusing using seismic migration techniques , 1991 .

[11]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[12]  A. Papoulis Systems and transforms with applications in optics , 1981 .

[13]  Jinsong Liu,et al.  An improved POCS super-resolution infrared image reconstruction algorithm based on visual mechanism , 2016 .

[14]  Daniel Gross,et al.  Improved resolution from subpixel shifted pictures , 1992, CVGIP Graph. Model. Image Process..

[15]  Michal Irani,et al.  Super-resolution from a single image , 2009, 2009 IEEE 12th International Conference on Computer Vision.