Mr. Paavo Hiltunen Profile

Paavo Hiltunen

at Aalto Univ

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Publications (2)

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Proceedings Article | 14 June 2023 Presentation + Paper

Imaging experiments with a 340-GHz FMCW radar and frequency-diverse holograms

Samu-Ville Pälli, Aleksi Tamminen, Paavo Hiltunen, Sazan Rexhepi, Marlene Bonmann, Tomas Bryllert, Duncan Robertson, Juha Ala-Laurinaho, Jan Stake, Zachary Taylor

Proceedings Volume 12535, 125350M (2023) https://doi.org/10.1117/12.2663757

KEYWORDS: Neural networks, Imaging systems, Holograms, Radar, Cameras, Visible radiation, Transceivers, Radar signal processing, Image restoration

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We present recent developments of a standoff imaging system based on a frequency-diverse phase hologram and deep neural networks. The single-pixel imaging system operates in a monostatic configuration consisting of a 340-GHz FMCW radar and a frequency-diverse phase hologram to interrogate the radar down range direction with spatially varying, frequency-dependent field patterns. The measured back-reflected signal contains spatial reflectivity information from the target, and the fast chirp rate of the radar enables real-time imaging performance. Together with simultaneously acquired visible-light images, a deep neural network integrated into the submillimeter-wave data readout electronics can map the received signal onto a 2D image without mechanical or active electrical beam scanning. In experiments, we have collected submillimeter-wave and visible-light data of a moving target in the region of interest with a 60-Hz frame rate. The results suggest that the system can image the moving target with a resolution comparable to the theoretical diffraction limit. The minimal hardware complexity and good imaging performance of the demonstrated computational submillimeter-wave imaging system support its potential as a cost-effective and easily deployable solution for various imaging applications.

Proceedings Article | 3 June 2022 Presentation + Paper

Submillimeter-wave holograms and imaging neural network: experiments at 220-330 GHz

Samu-Ville Pälli, Paavo Hiltunen, Milla Kosonen, Aleksi Tamminen, Juha Ala-Laurinaho, Zachary Taylor

Proceedings Volume 12111, 1211109 (2022) https://doi.org/10.1117/12.2618274

KEYWORDS: Holograms, Neural networks, Imaging systems

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We present the experimental results of a submillimeter-wave standoff imaging system based on a frequency-diverse hologram and image reconstruction via machine learning at 220-330 GHz. The imaging system operates in a single-pixel, monostatic configuration consisting of a transceiver together with a frequency-diverse phase hologram to interrogate the region of interest with quasi-random field patterns. The spatial reflectivity distribution in the region of interest is embedded in the wide-band frequency spectrum of the back-reflected signal and the images are acquired without mechanical or electrical scanning. Images from a visible-light camera are used as the ground truth of the target elements. The targets are scanned in the region of interest, while the wide-band reflection spectrum for the target is measured. The collected image-signal pair data are used to train a deconvolutional neural network for image reconstruction with the submillimeter-wave reflection spectra as input. In experiments, a corner-cube reflector and a complex test target made of copper foam were imaged in a 28-degree field of view at a distance of 600 mm from the imaging system. The effect of bandwidth on image quality is evaluated using 10-40 GHz bandwidths centered at 275 GHz to image the copper foam target. The resolution in the image predictions was estimated from fitted point-spread functions to be from 12 mm to 30 mm, with the highest resolution at the broadest bandwidth. We have correlated the measured field patterns at the region of interest with the mean squared error (MSE) of the predicted corner-cube images to analyze the effect of field characteristics on imaging accuracy. The results demonstrate increased accuracy in locations with high electric field amplitude and variation over the imaging bandwidth.

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