MIMO Radar-Based System for Structural Health Monitoring and Geophysical Applications
The paper presents a methodology for real-time structural health monitoring and geophysical applications. The key elements of the system are a high performance MIMO RADAR sensor, an optical camera and a dedicated set of software algorithms encompassing interferometry, tomography and photogrammetry. The MIMO Radar sensor proposed in this work, provides an extremely high sensitivity to displacements making the system able to react to tiny deformations (up to tens of microns) with a time scale which spans from milliseconds to hours. The MIMO feature of the system makes the system capable of providing a set of two-dimensional images of the observed scene, each mapped on the azimuth-range directions with noticeably resolution in both the dimensions and with an outstanding repetition rate. The back-scattered energy, which is distributed in the 3D space, is projected on a 2D plane, where each pixel has as coordinates the Line-Of-Sight distance and the cross-range azimuthal angle. At the same time, the high performing processing unit allows to sense the observed scene with remarkable refresh periods (up to milliseconds), thus opening the way for combined static and dynamic structural health monitoring. Thanks to the smart TX/RX antenna array layout, the MIMO data can be processed through a tomographic approach to reconstruct the three-dimensional map of the observed scene. This 3D point cloud is then accurately mapped on a 2D digital optical image through photogrammetric techniques, allowing for easy and straightforward interpretations of the measurements. Once the three-dimensional image is reconstructed, a 'repeat-pass' interferometric approach is exploited to provide the user of the system with high frequency three-dimensional motion/vibration estimation of each point of the reconstructed image. At this stage, the methodology leverages consolidated atmospheric correction algorithms to provide reliable displacement and vibration measurements.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.3298665Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 307
 A. Ferretti, G. Savio, R. Barzaghi, et al.: Submillimeter Accuracy of InSAR Time Series: Experimental Validation IEEE Transactions on Geoscience and Remote Sensing, vol. 45, pp. 1142-1153, May. 2007.
 P. Lombardo, D. Pastina and F. Turin, Ground Moving Target detection Based on MIMO SAR systems in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 8, no. 11, pp. 5081-5095, Nov. 2015.
 G. Krieger, T. Rommel and A. Moreira, MIMO-SAR Tomography Proceedings of EUSAR 2016: 11th European Conference on Synthetic Aperture Radar, Hamburg, Germany, 2016, pp. 1-6.
 D. Staglianò, E. Giusti, S. Lischi and M. Martorella, 3D InISAR-based target reconstruction algorithm by using a Multi-Channel ground-based radar demonstrator 2014 International Radar Conference, Lille, 2014, pp. 1-6.
 T. Rommel and G. Krieger, Detection of Multipath Propagation Effects in SAR-Tomography with MIMO Modes Proceedings of EUSAR 2016: 11th European Conference on Synthetic Aperture Radar, Hamburg, Germany, 2016, pp. 1-5.
 R. M. J. C. Curlander, Synthetic Aperture Radar - Systems and Signal Processing, Wiley, 1992.
 A. M. A. Reigber, First demonstration of airborne SAR tomography using multibaseline L-band data, IEEE Transaction Geoscience and Remote Sensing, vol. 38, n. 5, 2000.
 H. S. R. W. e. a. Y. Luo, Arc FMCW SAR and Applications in Ground Monitoring, IEEE Transactions on Geoscience and Remote Sensing, vol. 9, n. 52, 2014.
 D. D'Aria, P. Falcone, L. Maggi and G. Amoroso, Advanced Calibration Techniques for MIMO Radar, EUSAR 2018; 12th European Conference on Synthetic Aperture Radar, Aachen, Germany, 2018, pp. 1-5.