摘要 :
Analysis of radar backscattering of moving objects is typically performed with spectral analysis, to isolate signal located at different Doppler frequencies. This approach applies in the hypotheses that, during the observation tim...
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Analysis of radar backscattering of moving objects is typically performed with spectral analysis, to isolate signal located at different Doppler frequencies. This approach applies in the hypotheses that, during the observation time, the target moves with uniform radial velocity and its range changes by several wavelengths. This hypothesis is not always verified, e.g. for physiological internal movements when observed at low frequencies. This paper presents a theoretical model to represent the radar backscattering of a moving object with special emphasis on sub-wavelength motion. Furthermore, matching of the model with experimental data is discussed.
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摘要 :
Geostationary Doppler weather radar (GDWR), which is a novel and challenging instrument concept, can provide reflectivity profiles and Doppler dynamic information of meteorological targets over a circular disk coverage of approxim...
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Geostationary Doppler weather radar (GDWR), which is a novel and challenging instrument concept, can provide reflectivity profiles and Doppler dynamic information of meteorological targets over a circular disk coverage of approximately 5300km in diameter on the earth. In this paper, we estimate the mean Doppler radial velocity and Doppler spectrum width of GDWR, which have not been studied in the literature. We first calculate the relevant GDWR system parameters, and then investigate the accuracy of the mean Doppler radial velocity and Doppler spectrum width measurements using discrete Fourier transform and pulse pair methods. Simulation results show that the estimation performance is limited by the large normalized spectrum width of the echo when there is wind shear in the radar resolution volume. Proposals of improving the accuracy of the mean Doppler radial velocity and Doppler spectrum width estimates are also suggested.
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摘要 :
Geostationary Doppler weather radar (GDWR), which is a novel and challenging instrument concept, can provide reflectivity profiles and Doppler dynamic information of meteorological targets over a circular disk coverage of approxim...
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Geostationary Doppler weather radar (GDWR), which is a novel and challenging instrument concept, can provide reflectivity profiles and Doppler dynamic information of meteorological targets over a circular disk coverage of approximately 5300km in diameter on the earth. In this paper, we estimate the mean Doppler radial velocity and Doppler spectrum width of GDWR, which have not been studied in the literature. We first calculate the relevant GDWR system parameters, and then investigate the accuracy of the mean Doppler radial velocity and Doppler spectrum width measurements using discrete Fourier transform and pulse pair methods. Simulation results show that the estimation performance is limited by the large normalized spectrum width of the echo when there is wind shear in the radar resolution volume. Proposals of improving the accuracy of the mean Doppler radial velocity and Doppler spectrum width estimates are also suggested.
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摘要 :
Millimeter-wave radar is currently the most effective automotive sensor capable of all-weather perception. In order to detect Vulnerable Road Users (VRUs) in cluttered radar data, it is necessary to model the time-frequency signal...
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Millimeter-wave radar is currently the most effective automotive sensor capable of all-weather perception. In order to detect Vulnerable Road Users (VRUs) in cluttered radar data, it is necessary to model the time-frequency signal patterns of human motion, i.e. the micro-Doppler signature. In this paper we propose a spatio-temporal Convolutional Neural Network (CNN) capable of detecting VRUs in cluttered radar data. The main contribution is a weakly supervised training method which uses abundant, automatically generated labels from camera and lidar for training the model. The input to the network is a tensor of temporally concatenated range-azimuth-Doppler arrays, while the ground truth is an occupancy grid formed by objects detected jointly in-camera images and lidar. Lidar provides accurate ranging ground truth, while camera information helps distinguish between VRUs and background. Experimental evaluation shows that the CNN model has superior detection performance compared to classical techniques. Moreover, the model trained with imperfect, weak supervision labels outperforms the one trained with a limited number of perfect, hand-annotated labels. Finally, the proposed method has excellent scalability due to the low cost of automatic annotation.
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摘要 :
Millimeter-wave radar is currently the most effective automotive sensor capable of all-weather perception. In order to detect Vulnerable Road Users (VRUs) in cluttered radar data, it is necessary to model the time-frequency signal...
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Millimeter-wave radar is currently the most effective automotive sensor capable of all-weather perception. In order to detect Vulnerable Road Users (VRUs) in cluttered radar data, it is necessary to model the time-frequency signal patterns of human motion, i.e. the micro-Doppler signature. In this paper we propose a spatio-temporal Convolutional Neural Network (CNN) capable of detecting VRUs in cluttered radar data. The main contribution is a weakly supervised training method which uses abundant, automatically generated labels from camera and lidar for training the model. The input to the network is a tensor of temporally concatenated range-azimuth-Doppler arrays, while the ground truth is an occupancy grid formed by objects detected jointly in-camera images and lidar. Lidar provides accurate ranging ground truth, while camera information helps distinguish between VRUs and background. Experimental evaluation shows that the CNN model has superior detection performance compared to classical techniques. Moreover, the model trained with imperfect, weak supervision labels outperforms the one trained with a limited number of perfect, hand-annotated labels. Finally, the proposed method has excellent scalability due to the low cost of automatic annotation.
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This paper introduces a non-parametric machine learning algorithm for dual-polarization radar rainfall estimation. The machine learning model is trained and tested using pseudo radar observations simulated using in situ raindrop s...
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This paper introduces a non-parametric machine learning algorithm for dual-polarization radar rainfall estimation. The machine learning model is trained and tested using pseudo radar observations simulated using in situ raindrop size distribution data. Preliminary results show the superior performance of the proposed approach to traditional parametric radar rainfall relations.
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摘要 :
This paper introduces a non-parametric machine learning algorithm for dual-polarization radar rainfall estimation. The machine learning model is trained and tested using pseudo radar observations simulated using in situ raindrop s...
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This paper introduces a non-parametric machine learning algorithm for dual-polarization radar rainfall estimation. The machine learning model is trained and tested using pseudo radar observations simulated using in situ raindrop size distribution data. Preliminary results show the superior performance of the proposed approach to traditional parametric radar rainfall relations.
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In this paper, a staggered Pulse Repetition Frequency (PRF) coherent integration based on phase/time compensation algorithm in the receiver is proposed. Two staggered PRFs are used to extend blind Doppler frequency. The phase/time...
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In this paper, a staggered Pulse Repetition Frequency (PRF) coherent integration based on phase/time compensation algorithm in the receiver is proposed. Two staggered PRFs are used to extend blind Doppler frequency. The phase/time compensation algorithm is applied for coherent integration of non-coherent pulse-trains. As a result, the Doppler frequencies of targets are determined.
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摘要 :
In this paper, a staggered Pulse Repetition Frequency (PRF) coherent integration based on phase/time compensation algorithm in the receiver is proposed. Two staggered PRFs are used to extend blind Doppler frequency. The phase/time...
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In this paper, a staggered Pulse Repetition Frequency (PRF) coherent integration based on phase/time compensation algorithm in the receiver is proposed. Two staggered PRFs are used to extend blind Doppler frequency. The phase/time compensation algorithm is applied for coherent integration of non-coherent pulse-trains. As a result, the Doppler frequencies of targets are determined.
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摘要 :
This paper proposes a new approach for improving the ranging precision using adjustment algorithm with doppler measurements. As a data processing technique, the proposed approach requires no additional supports of specifically des...
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This paper proposes a new approach for improving the ranging precision using adjustment algorithm with doppler measurements. As a data processing technique, the proposed approach requires no additional supports of specifically designed hardware or software, which mainly takes advantages of the accurate doppler measurements. Simulations have been conducted and convincible results have been obtained, which validates the effectiveness of the proposed approach. Results also show that weights for doppler measurements should be carefully treated in practice, which would significantly affect the precision and coherency.
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