Tor Berger

• Stack, Kathryn M.; Williams, Nathan R; Calef III, Fred; Sun, Vivian Z; Williford, Kenneth H & Farley, Kenneth A. [Show all 65 contributors for this article] (2020). Photogeologic Map of the Perseverance Rover Field Site in Jezero Crater Constructed by the Mars 2020 Science Team. Space Science Reviews. ISSN 0038-6308. 216. doi: 10.1007/s11214-020-00739-x.
• Hamran, Svein-Erik; Paige, David A.; Amundsen, Hans E. Foss; Berger, Tor; Brovoll, Sverre & Carter, Lynn [Show all 14 contributors for this article] (2020). Radar imager for Mars’ subsurface experiment—RIMFAX. Space Science Reviews. ISSN 0038-6308. 216(8). doi: 10.1007/s11214-020-00740-4. Full text in Research Archive
• Aardal, Øyvind; Brovoll, Sverre; Berger, Tor; Balasingham, Ilangko & Hamran, Svein-Erik (2017). Phase distortions in radar respiration monitoring, 2017 First IEEE MTT-S International Microwave Bio Conference (IMBIOC). IEEE. ISSN 978-1-5386-1713-7. doi: 10.1109/IMBIOC.2017.7965778.
• Aardal, Øyvind; Brovoll, Sverre; Paichard, Yoann; Berger, Tor; Lande, Tor Sverre & Hamran, Svein-Erik (2015). Radar sensitivity to human heartbeats and respiration. Proceedings of SPIE, the International Society for Optical Engineering. ISSN 0277-786X. 9461. doi: 10.1117/12.2180924.
• (2015). Harmonic Synthetic Aperture Radar Processing. IEEE Geoscience and Remote Sensing Letters. ISSN 1545-598X. 12(10), p. 2066–2069. doi: 10.1109/LGRS.2015.2447517.
• Solberg, Lars Erik; Aardal, Øyvind; Berger, Tor; Balasingham, Ilangko; Fosse, Erik & Hamran, Svein-Erik (2015). Experimental investigation into radar-based central blood pressure estimation. IET Radar, Sonar & Navigation. ISSN 1751-8784. 9(2), p. 145–153. doi: 10.1049/iet-rsn.2014.0206. Full text in Research Archive
• Brovoll, Sverre; Berger, Tor; Paichard, Yoann; Aardal, Øyvind; Lande, Tor Sverre & Hamran, Svein-Erik (2014). Time-lapse imaging of human heart motion with switched array UWB radar. IEEE Transactions on Biomedical Circuits and Systems. ISSN 1932-4545. 8(5), p. 704–715. doi: 10.1109/TBCAS.2014.2359995.
• Øyan, Mats Jørgen; Hamran, Svein-Erik; Damsgård, Leif & Berger, Tor (2014). Compact airborne C-band radar sounder. IEEE Transactions on Geoscience and Remote Sensing. ISSN 0196-2892. 52(10), p. 6326–6332. doi: 10.1109/TGRS.2013.2296074.
• Aardal, Øyvind; Paichard, Yoann; Brovoll, Sverre; Berger, Tor; Lande, Tor Sverre & Hamran, Svein-Erik (2013). Physical Working Principles of Medical Radar. IEEE Transactions on Biomedical Engineering. ISSN 0018-9294. 60(4), p. 1142–1149. doi: 10.1109/TBME.2012.2228263.
• (2012). Experiments with autofocus for strip map synthetic aperture radar data. IEEE International Conference on Radar (RADAR). ISSN 1097-5764. doi: 10.1109/RADAR.2012.6212108.
• Øyan, Mats Jørgen; Hamran, Svein-Erik; Hanssen, Leif; Berger, Tor & Plettemeier, Dirk (2012). Ultrawideband Gated Step Frequency Ground-Penetrating Radar. IEEE Transactions on Geoscience and Remote Sensing. ISSN 0196-2892. 50(1), p. 212–220. doi: 10.1109/TGRS.2011.2160069.
• Aardal, Øyvind; Hamran, Svein-Erik; Berger, Tor; Paichard, Yoann & Lande, Tor Sverre (2011). Chest movement estimation from radar modulation caused by heartbeats. In Cauwenberghs, G. (Eds.), IEEE Biomedical Circuits and Systems Conference 2011. IEEE conference proceedings. ISSN 978-1-4577-1469-6. p. 422–425. doi: 10.1109/biocas.2011.6107825.
• (2011). Comparison of two methods for automatic range alignment in ISAR maging. IEEE International Conference on Radar (RADAR). ISSN 1097-5764. p. 6–11. doi: 10.1109/RADAR.2011.5960489.
• Aardal, Øyvind; Hamran, Svein-Erik; Berger, Tor; Hammerstad, Jan & Lande, Tor Sverre (2011). Radar cross section of the human heartbeat and respiration in the 500MHz to 3GHz band. In Ponchak, George E. (Eds.), Radio and Wireless Symposium (RWS), 2011 IEEE. IEEE conference proceedings. ISSN 978-1-4244-7687-9. p. 422–425. doi: 10.1109/rws.2011.5725466.
• (2010). An efficient scaled maximum likelihood algorithm for translational motion estimation in ISAR imaging. IEEE International Conference on Radar (RADAR). ISSN 1097-5764. p. 75–80.
• Aardal, Øyvind; Hamran, Svein-Erik; Berger, Tor; Hammerstad, Jan & Lande, Tor Sverre (2010). Radar Cross Section of the Human Heartbeat and Respiration. In Georgiou, Julius (Eds.), Proceedings of BioCAS 2010. IEEE conference proceedings. ISSN 9781424472680.
• Solberg, Lars Erik; Hamran, Svein-Erik; Berger, Tor & Balasingham, Ilangko (2010). Minimum Variance Signal Selection for Aorta Radius Estimation Using Radar. EURASIP Journal on Advances in Signal Processing. ISSN 1687-6172. doi: 10.1155/2010/682037. Full text in Research Archive

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• Aardal, Øyvind; Brovoll, Sverre; Berger, Tor; Balasingham, Ilangko & Hamran, Svein-Erik (2017). Phase distortions in radar respiration monitoring.
• (2015). Target classification challenges in modern long range air defense radar.
• Berger, Tor; Hamran, Svein-Erik; Ødegaard, Nina; Øyan, Mats Jørgen & Damsgård, Leif (2015). Stripmap Autofocus of Short Range Ku-band Synthetic Aperture Radar Data.
• Brovoll, Sverre; Berger, Tor; Aardal, Øyvind; Lande, Tor Sverre & Hamran, Svein-Erik (2014). Distortion effects in a switch array UWB radar for time-lapse imaging of human heartbeats.
• Aardal, Øyvind; Brovoll, Sverre; Paichard, Yoann; Berger, Tor; Lande, Tor Sverre & Hamran, Svein-Erik (2013). Detecting changes in the human heartbeat with on-body radar.
• Brovoll, Sverre; Berger, Tor; Paichard, Yoann; Aardal, Øyvind; Lande, Tor Sverre & Hamran, Svein-Erik (2013). Time-lapse imaging of human heartbeats using UWB radar.
• Brovoll, Sverre; Aardal, Øyvind; Paichard, Yoann; Berger, Tor; Lande, Tor Sverre & Hamran, Svein-Erik (2013). Optimal frequency range for medical radar measurements of human heartbeats using body-contact radar.
• (2010). An efficient scaled maximum likelihood algorithm for translational motion estimation in ISAR imaging. Show summary

  In ISAR imaging, the relative motion between the target and the radar must be known precisely to produce focused radar images. The translational motion of the target must be compensated for to use only the rotational motion around a fixed centre point for the imaging of the target. An efficient implementation of a maximum likelihood (ML) algorithm for translational motion estimation based on the Chirp-Z transform is described. If the line of sight vector from the radar to the target is not within the rotational plane of the object, or the rotational plane changes during the observation time, and strong reflectors tend to bias the estimate of translational motion. A scaling of range profiles is shown to reduce the bias. The shear average algorithm is similar to the algorithm described here, but it only estimates translational motion to within half the carrier wavelength. Simulated and experimental data are used to show the effectiveness of the algorithm. An image sharpness measure is used to indicate the effects of scaling as a preprocessing step on experimental data. All results are compared to those obtained by shear average and prominent point processing techniques.

• (2010). ISAR imaging of small aircraft. Show summary

  In ISAR imaging, the relative motion between the target and the radar must be known precisely to produce focused radar images. The translational motion of the target must be compensated for in order to use only the rotational motion around a fixed centre point for the imaging of the target. Different methods for range alignment can be used, and the success depends much on the nature of the data. If strong reflectors are dominant throughout the observation period, a prominent point processing (PPP) method may produce good results. If not, other methods may apply. The advantage of using PPP is simplicity in concept and its processing speed. Another method, in general slower than PPP, is based on maximum likelihood estimation of translational motion. An maximum likelihood algorithm for translational motion estimation (TME) is described. Data from the German research radar TIRA at FGAN-FHR is processed to show the potential of the algorithms. Also, after range alignment, the kurtosis (fourth order central moment) of averaged range profiles gives an indication of which of the two methods that gives the best results in terms of image sharpness. Data from TIRA of a small aircraft is collected over a period of minutes and are processed in five second intervals with 80 % overlap. The results from the PPP and the TME algorithms are compared, automatically in terms of a sharpness measure, and also by user inspection. The results indicate that for low kurtosis regions the TME method should be used, while for high kurtosis regions, the PPP method is preferred. In this way the total processing time can be reduced for multi image sequences, as we use the PPP method whenever we can and the TME method whenever we have to.

• Aardal, Øyvind; Hamran, Svein-Erik; Lande, Tor Sverre & Berger, Tor (2013). Radar monitoring of heartbeats and respiration. Akademika forlag.

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