The Experts below are selected from a list of 26550 Experts worldwide ranked by ideXlab platform
Heng Zhang - One of the best experts on this subject based on the ideXlab platform.
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processing sliding mosaic mode data with modified full aperture imaging algorithm integrating scalloping correction
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017Co-Authors: Robert Wang, Yunkai Deng, Tuan Zhao, Wei Wang, Heng ZhangAbstract:Modified full-aperture imaging algorithm for sliding Mosaic mode synthetic aperture Radar (SAR) is presented in this paper, which includes scalloping correction and spikes suppression. The full-aperture imaging algorithm is introduced into Mosaic mode and validated by real C-band airborne SAR imaging experiments. The main idea is to substitute zeros between bursts with linear-predicted data extrapolated from adjacent bursts to suppress the spikes caused by multibursts processing. Furthermore, scalloping correction for sliding Mosaic mode is integrated with this algorithm. It is innovational to correct the azimuth beam pattern weighting altered by Radar Antenna rotation in azimuth with deramping preprocessing operation. Finally, experiments performed by the C-band airborne SAR system with a maximum bandwidth of 200 MHz validate the effectiveness of the approach.
Robert Wang - One of the best experts on this subject based on the ideXlab platform.
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processing sliding mosaic mode data with modified full aperture imaging algorithm integrating scalloping correction
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017Co-Authors: Robert Wang, Yunkai Deng, Tuan Zhao, Wei Wang, Heng ZhangAbstract:Modified full-aperture imaging algorithm for sliding Mosaic mode synthetic aperture Radar (SAR) is presented in this paper, which includes scalloping correction and spikes suppression. The full-aperture imaging algorithm is introduced into Mosaic mode and validated by real C-band airborne SAR imaging experiments. The main idea is to substitute zeros between bursts with linear-predicted data extrapolated from adjacent bursts to suppress the spikes caused by multibursts processing. Furthermore, scalloping correction for sliding Mosaic mode is integrated with this algorithm. It is innovational to correct the azimuth beam pattern weighting altered by Radar Antenna rotation in azimuth with deramping preprocessing operation. Finally, experiments performed by the C-band airborne SAR system with a maximum bandwidth of 200 MHz validate the effectiveness of the approach.
Yonina C Eldar - One of the best experts on this subject based on the ideXlab platform.
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cognitive Radar Antenna selection via deep learning
Iet Radar Sonar and Navigation, 2019Co-Authors: Ahmet M Elbir, Kumar Vijay Mishra, Yonina C EldarAbstract:Direction-of-arrival (DoA) estimation of targets improves with the number of elements employed by a phased array Radar Antenna. Since larger arrays have high associated cost, area and computational load, there is a recent interest in thinning the Antenna arrays without loss of far-field DoA accuracy. In this context, a cognitive Radar may deploy a full array and then select an optimal subarray to transmit and receive the signals in response to changes in the target environment. Prior works have used optimisation and greedy search methods to pick the best subarrays cognitively. In this study, deep learning is leveraged to address the Antenna selection problem. Specifically, they construct a convolutional neural network (CNN) as a multi-class classification framework, where each class designates a different subarray. The proposed network determines a new array every time data is received by the Radar, thereby making Antenna selection a cognitive operation. Their numerical experiments show that the proposed CNN structure provides 22% better classification performance than a support vector machine and the resulting subarrays yield 72% more accurate DoA estimates than random array selections.
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cognitive Radar Antenna selection via deep learning
arXiv: Signal Processing, 2018Co-Authors: Ahmet M Elbir, Kumar Vijay Mishra, Yonina C EldarAbstract:Direction of arrival (DoA) estimation of targets improves with the number of elements employed by a phased array Radar Antenna. Since larger arrays have high associated cost, area and computational load, there is recent interest in thinning the Antenna arrays without loss of far-field DoA accuracy. In this context, a cognitive Radar may deploy a full array and then select an optimal subarray to transmit and receive the signals in response to changes in the target environment. Prior works have used optimization and greedy search methods to pick the best subarrays cognitively. In this paper, we leverage deep learning to address the Antenna selection problem. Specifically, we construct a convolutional neural network (CNN) as a multi-class classification framework where each class designates a different subarray. The proposed network determines a new array every time data is received by the Radar, thereby making Antenna selection a cognitive operation. Our numerical experiments show that {the proposed CNN structure provides 22% better classification performance than a Support Vector Machine and the resulting subarrays yield 72% more accurate DoA estimates than random array selections.
Tuan Zhao - One of the best experts on this subject based on the ideXlab platform.
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processing sliding mosaic mode data with modified full aperture imaging algorithm integrating scalloping correction
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017Co-Authors: Robert Wang, Yunkai Deng, Tuan Zhao, Wei Wang, Heng ZhangAbstract:Modified full-aperture imaging algorithm for sliding Mosaic mode synthetic aperture Radar (SAR) is presented in this paper, which includes scalloping correction and spikes suppression. The full-aperture imaging algorithm is introduced into Mosaic mode and validated by real C-band airborne SAR imaging experiments. The main idea is to substitute zeros between bursts with linear-predicted data extrapolated from adjacent bursts to suppress the spikes caused by multibursts processing. Furthermore, scalloping correction for sliding Mosaic mode is integrated with this algorithm. It is innovational to correct the azimuth beam pattern weighting altered by Radar Antenna rotation in azimuth with deramping preprocessing operation. Finally, experiments performed by the C-band airborne SAR system with a maximum bandwidth of 200 MHz validate the effectiveness of the approach.
Yunkai Deng - One of the best experts on this subject based on the ideXlab platform.
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processing sliding mosaic mode data with modified full aperture imaging algorithm integrating scalloping correction
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017Co-Authors: Robert Wang, Yunkai Deng, Tuan Zhao, Wei Wang, Heng ZhangAbstract:Modified full-aperture imaging algorithm for sliding Mosaic mode synthetic aperture Radar (SAR) is presented in this paper, which includes scalloping correction and spikes suppression. The full-aperture imaging algorithm is introduced into Mosaic mode and validated by real C-band airborne SAR imaging experiments. The main idea is to substitute zeros between bursts with linear-predicted data extrapolated from adjacent bursts to suppress the spikes caused by multibursts processing. Furthermore, scalloping correction for sliding Mosaic mode is integrated with this algorithm. It is innovational to correct the azimuth beam pattern weighting altered by Radar Antenna rotation in azimuth with deramping preprocessing operation. Finally, experiments performed by the C-band airborne SAR system with a maximum bandwidth of 200 MHz validate the effectiveness of the approach.