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Using millimeter wave (mmWave) signals for imaging has an important advantage in that they can penetrate through poor environmental conditions such as fog, dust, and smoke that severely degrade optical-based imaging systems. However, mmWave radars, contrary to cameras and LiDARs, suffer from low angular resolution because of small physical apertures and conventional signal processing techniques. Sparse radar imaging, on the other hand, can increase the aperture size while minimizing the power consumption and read out bandwidth. This paper presents CoIR, an analysis by synthesis method that leverages the implicit neural network bias in convolutional decoders and compressed sensing to perform high accuracy sparse radar imaging. The proposed system is data set-agnostic and does not require any auxiliary sensors for training or testing. We introduce a sparse array design that allows for a 5.5× reduction in the number of antenna elements needed compared to conventional MIMO array designs. We demonstrate our system's improved imaging performance over standard mmWave radars and other competitive untrained methods on both simulated and experimental mmWave radar data.
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Aperture Synthesis Imaging of Ionospheric Irregularities Using Time Diversity MIMO Radar
Abstract Aperture‐synthesis images of ionospheric irregularities in the equatorial electrojet are computed using multiple‐input multiple‐output (MIMO) radar methods at the Jicamarca Radio Observatory. MIMO methods increase the number of distinct interferometry baselines available for imaging (by a factor of essentially three in these experiments) as well as the overall size of the synthetic aperture. The particular method employed here involves time‐division multiplexing or time diversity to distinguish pulses transmitted from different quarters of the Jicamarca array. The method comes at the cost of a large increase in computation time and complexity and a reduced signal‐to‐noise ratio. We discuss the details involved in the signal processing and the trade space involved in image optimization.
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- Award ID(s):
- 2213849
- PAR ID:
- 10465688
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Radio Science
- Volume:
- 58
- Issue:
- 9
- ISSN:
- 0048-6604
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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