This paper dwells upon optimizing the azimuth samp-ling interval of digital surface maps used to model radar ground clutter.The resulting equations can be used to find the digital map sampling interval for the require...This paper dwells upon optimizing the azimuth samp-ling interval of digital surface maps used to model radar ground clutter.The resulting equations can be used to find the digital map sampling interval for the required calculation error and modeled power of the simulated signal,which determines the resulting distribution of backscatter intensity.The paper further showcases how the sampling interval could be increased by pre-processing the map.展开更多
A novel clutter suppression method in ground penetrating radar （GPR） is proposed. The preliminary result is obtained by using target resolution improved processing （TRIP）. The preliminary result will be used as an...A novel clutter suppression method in ground penetrating radar （GPR） is proposed. The preliminary result is obtained by using target resolution improved processing （TRIP）. The preliminary result will be used as an initial input for TRIP iteration. All TRIP iteration steps are the adaptive linear combination of the previous TRIP result and the preliminary result. This adaptive combination strategy can balance clutter suppression and target information protection, which is considered as a troublesome contradiction and a chronic problem in clutter suppression research. When the matrix entropy of iteration result converges, the algorithm can achieve a good result both in clutter suppression and target protection. Experimental results demonstrate that the new algorithm outperforms the existing approaches.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
基金supported by the Russian Foundation for Basic Research(19-37-90103).
文摘This paper dwells upon optimizing the azimuth samp-ling interval of digital surface maps used to model radar ground clutter.The resulting equations can be used to find the digital map sampling interval for the required calculation error and modeled power of the simulated signal,which determines the resulting distribution of backscatter intensity.The paper further showcases how the sampling interval could be increased by pre-processing the map.
基金supported by the National Natural Science Foundation of China under Grant No. 40976114the National 863 Program under Grant No. 2008AA121702-3
文摘A novel clutter suppression method in ground penetrating radar （GPR） is proposed. The preliminary result is obtained by using target resolution improved processing （TRIP）. The preliminary result will be used as an initial input for TRIP iteration. All TRIP iteration steps are the adaptive linear combination of the previous TRIP result and the preliminary result. This adaptive combination strategy can balance clutter suppression and target information protection, which is considered as a troublesome contradiction and a chronic problem in clutter suppression research. When the matrix entropy of iteration result converges, the algorithm can achieve a good result both in clutter suppression and target protection. Experimental results demonstrate that the new algorithm outperforms the existing approaches.
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.
文摘高频地波雷达是海上动目标检测的重要手段,其中海杂波是影响海面目标检测性能的主要因素。为了提高海杂波的预测精度进而有效抑制海杂波,本文提出了一种基于改进蚁狮算法(Ant Lion Optimizer,ALO)优化RBF神经网络的海杂波预测模型(MGPALO-RBF,Multiple elites dynamic guidance Ant Lion Optimizer based on Gaussian difference variation-based learning with Perturbation factor-radial basis function)。由于标准蚁狮算法具有易陷入局部最优且收敛速度慢的缺点,本文在蚂蚁进行随机行走的过程中加入扰动因子以增加种群的活跃性和多样性,并提出多个精英动态引导机制,强化算法前期的探索能力和后期的开发能力,同时对种群中较差蚁狮进行高斯差分变异以提高算法的收敛速度。仿真结果表明:改进的蚁狮算法在对比算法中具有更高的收敛精度和收敛速度,MGPALO-RBF模型具有更好的海杂波预测性能。
