This paper presents a modified frequency scaling algorithm for frequency modulated continuous wave synthetic aperture radar （FMCW SAR） data processing. The relative motion between radar and target in FMCW SAR during...This paper presents a modified frequency scaling algorithm for frequency modulated continuous wave synthetic aperture radar （FMCW SAR） data processing. The relative motion between radar and target in FMCW SAR during reception and between transmission and reception will introduce serious dilation in the received signal. The dilation can cause serious distortions in the reconstructed images using conventional signal processing methods. The received signal is derived and the received signal in range-Doppler domain is given. The relation between the phase resulting from antenna motion and the azimuth frequency is analyzed. The modified frequency scaling algorithm is proposed to process the received signal with serious dilation. The algorithm can effectively eliminate the impact of the dilation. The algorithm performances are shown by the simulation results.展开更多
Considering the problem that the scattering echo images of airborne Doppler weather radar are often reduced by ground clutters,the accuracy and confidence of meteorology target detection are reduced.In this paper,a de...Considering the problem that the scattering echo images of airborne Doppler weather radar are often reduced by ground clutters,the accuracy and confidence of meteorology target detection are reduced.In this paper,a deep convolutional neural network(DCNN)is proposed for meteorology target detection and ground clutter suppression with a large collection of airborne weather radar images as network input.For each weather radar image,the corresponding digital elevation model(DEM)image is extracted on basis of the radar antenna scan-ning parameters and plane position,and is further fed to the net-work as a supplement for ground clutter suppression.The fea-tures of actual meteorology targets are learned in each bottle-neck module of the proposed network and convolved into deeper iterations in the forward propagation process.Then the network parameters are updated by the back propagation itera-tion of the training error.Experimental results on the real mea-sured images show that our proposed DCNN outperforms the counterparts in terms of six evaluation factors.Meanwhile,the network outputs are in good agreement with the expected mete-orology detection results(labels).It is demonstrated that the pro-posed network would have a promising meteorology observa-tion application with minimal effort on network variables or parameter changes.展开更多
文摘This paper presents a modified frequency scaling algorithm for frequency modulated continuous wave synthetic aperture radar （FMCW SAR） data processing. The relative motion between radar and target in FMCW SAR during reception and between transmission and reception will introduce serious dilation in the received signal. The dilation can cause serious distortions in the reconstructed images using conventional signal processing methods. The received signal is derived and the received signal in range-Doppler domain is given. The relation between the phase resulting from antenna motion and the azimuth frequency is analyzed. The modified frequency scaling algorithm is proposed to process the received signal with serious dilation. The algorithm can effectively eliminate the impact of the dilation. The algorithm performances are shown by the simulation results.
基金supported by the China Ministry of Industry and Information Technology Foundation and Aeronautical Science Foundation of China(ASFC-201920007002)the National Key Research and Development Plan(2021YFB1600603)the Open Fund of Key Laboratory of Civil Aircraft Airworthiness Technology,Civil Aviation University of China.
文摘Considering the problem that the scattering echo images of airborne Doppler weather radar are often reduced by ground clutters,the accuracy and confidence of meteorology target detection are reduced.In this paper,a deep convolutional neural network(DCNN)is proposed for meteorology target detection and ground clutter suppression with a large collection of airborne weather radar images as network input.For each weather radar image,the corresponding digital elevation model(DEM)image is extracted on basis of the radar antenna scan-ning parameters and plane position,and is further fed to the net-work as a supplement for ground clutter suppression.The fea-tures of actual meteorology targets are learned in each bottle-neck module of the proposed network and convolved into deeper iterations in the forward propagation process.Then the network parameters are updated by the back propagation itera-tion of the training error.Experimental results on the real mea-sured images show that our proposed DCNN outperforms the counterparts in terms of six evaluation factors.Meanwhile,the network outputs are in good agreement with the expected mete-orology detection results(labels).It is demonstrated that the pro-posed network would have a promising meteorology observa-tion application with minimal effort on network variables or parameter changes.
