3D Imaging Millimeter Wave Circular Synthetic Aperture Radar

Zhang, Renyuan; Cao, Siyang

doi:10.20944/preprints201703.0103.v1

2017

DOI: 10.20944/preprints201703.0103.v1

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3D Imaging Millimeter Wave Circular Synthetic Aperture Radar

Renyuan Zhang

Siyang Cao

Abstract: Abstract:In this paper, a new millimeter wave 3D imaging radar is proposed. The user just needs to move the radar along a circular track, a high resolution 3D imaging can be generated. The proposed radar uses the movement of itself to synthesize a large aperture in both the azimuth and elevation directions. It can utilize inverse Radon transform to resolve 3D imaging. To improve the sensing result, compressed sensing approach is further investigated. The simulation and experimental result further illustrated t… Show more

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Cited by 7 publications

References 25 publications

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mmSight: A Robust Millimeter-Wave Near-Field SAR Imaging Algorithm

et al. 2022

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Millimeter-wave SAR (Synthetic Aperture Radar) imaging is widely studied as a common means of RF (Radio Frequency) imaging, but there are problems of the ghost image in Sparsely-Sampled cases and the projection of multiple targets at different distances. Therefore, a robust imaging algorithm based on the Analytic Fourier Transform is proposed, which is named mmSight. First, the original data are windowed with Blackman window to take multiple distance planes into account; then, the Analytic Fourier Transform that can effectively suppress the ghost image under Sparsely-Sampled is used for imaging; finally, the results are filtered using a Mean Filter to remove spatial noise. The experimental results show that the proposed imaging algorithm in this paper, relative to other algorithms, can image common Fully-Sampled single target, hidden target, and multiple targets at the same distance, and solve the ghost image problem of single target in the case of Sparsely-Sampled, as well as the projection problem of multiple targets at different distances; the Image Entropy of the mmSight is 4.6157 and is on average 0.3372 lower than that of other algorithms. Compared with other algorithms, the sidelobe and noise of the Point Spread Function are suppressed, so the quality of the image obtained from imaging is better than that of other algorithms.

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mmSight: A Robust Millimeter-Wave Near-Field SAR Imaging Algorithm

et al. 2022

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Improved ISRJ-Based Radar Target Echo Cancellation Using Frequency Shifting Modulation

Zhao

Wang

et al. 2019

Electronics

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Target echo cancellation is an ingenious method that protects the target of interest (TOI) from being detected by radar. Interrupted-sampling repeater jamming (ISRJ) is a novel deception jamming method for linear frequency modulation (LFM) radar countermeasures, which has been applied in target echo cancellation recently. Compared with the conventional cancellation method, not only can the target echo be successfully cancelled at radar receiver, but a train of false targets is also produced and forms deception jamming by applying the ISRJ technique. In this paper, an improved radar target echo cancellation method based on ISRJ is proposed that utilizes an extra frequency shifting modulation on the intercepted LFM radar signal. The jammer power is more efficiently utilized by the proposed method. Moreover, more flexible multi-false-target deception jamming can be obtained by adjusting the interrupted sampling frequency. The real target remains effectively protected by the false preceding target in the presence of amplitude mismatch of cancellation signal and target echo. Numerical simulations and measured data experiments are conducted to demonstrate the effectiveness of the proposed method.

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Sparse Optical Flow Implementation Using a Neural Network for Low-Resolution Thermal Aerial Imaging

Nguyen

Chahl

2022

J. Imaging

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This study is inspired by the widely used algorithm for real-time optical flow, the sparse Lucas–Kanade, by applying a feature extractor to decrease the computational requirement of optical flow based neural networks from real-world thermal aerial imagery. Although deep-learning-based algorithms have achieved state-of-the-art accuracy and have outperformed most traditional techniques, most of them cannot be implemented on a small multi-rotor UAV due to size and weight constraints on the platform. This challenge comes from the high computational cost of these techniques, with implementations requiring an integrated graphics processing unit with a powerful on-board computer to run in real time, resulting in a larger payload and consequently shorter flight time. For navigation applications that only require a 2D optical flow vector, a dense flow field computed from a deep learning neural network contains redundant information. A feature extractor based on the Shi–Tomasi technique was used to extract only appropriate features from thermal images to compute optical flow. The state-of-the-art RAFT-s model was trained with a full image and with our proposed alternative input, showing a substantial increase in speed while maintain its accuracy in the presence of high thermal contrast where features could be detected.

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3D Imaging Millimeter Wave Circular Synthetic Aperture Radar

Cited by 7 publications

References 25 publications

mmSight: A Robust Millimeter-Wave Near-Field SAR Imaging Algorithm

mmSight: A Robust Millimeter-Wave Near-Field SAR Imaging Algorithm

Improved ISRJ-Based Radar Target Echo Cancellation Using Frequency Shifting Modulation

Sparse Optical Flow Implementation Using a Neural Network for Low-Resolution Thermal Aerial Imaging

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