高频地波雷达的探测性能极易受到射频干扰的影响,当前射频干扰抑制的研究主要是通过人工识别来逐一处理,鲜见实时自动识别与抑制射频干扰的研究。随着深度学习在雷达图像处理方面应用的展开,本文尝试将其引入高频雷达射频干扰抑制中,利...高频地波雷达的探测性能极易受到射频干扰的影响,当前射频干扰抑制的研究主要是通过人工识别来逐一处理,鲜见实时自动识别与抑制射频干扰的研究。随着深度学习在雷达图像处理方面应用的展开,本文尝试将其引入高频雷达射频干扰抑制中,利用YOLO(You Only Look Once)模型来识别雷达距离多普勒谱图中的射频干扰,继而用高阶奇异值分解(Higher Order Singular Value Decomposition,HOSVD)方法对其进行抑制。仿真和实测数据处理结果表明,此YOLO-HOSVD联合算法实现了对高频雷达射频干扰的自动识别与抑制,单场数据处理时间不超过1.8 s。该方法可以应用于高频地波雷达常规海态观测。展开更多
为抑制海杂波检测高频地波雷达(high frequency surface wave radar,HFSWR)中的目标,提出了基于零陷展宽(null widening,NW)的空域海杂波抑制算法.NW算法利用海洋运动的连续性和海杂波的弥散性,基于干扰位置变化规律构造加权干扰NW矩阵...为抑制海杂波检测高频地波雷达(high frequency surface wave radar,HFSWR)中的目标,提出了基于零陷展宽(null widening,NW)的空域海杂波抑制算法.NW算法利用海洋运动的连续性和海杂波的弥散性,基于干扰位置变化规律构造加权干扰NW矩阵,从阵列中抑制海杂波并保留目标.与其他海杂波抑制算法对比,利用新的波束形成方法将干扰零陷方向展宽,不改变噪声项贡献,从而有效抑制海杂波.利用NW算法在空域抑制海杂波时,根据实际应用环境计算修正矩阵,在实时信号处理过程中不仅运算速度快,而且提高了输出信号杂波噪声比(signal to clutter plus noise ratio,SCNR),针对海杂波抑制效果较好,有助于后续信号处理目标检测工作.展开更多
In this study, we explored the neural mechanism of global topological perception in the human visual system. We showed strong evidence that the retinotectal pathway in the archicortex of the human brain is responsible...In this study, we explored the neural mechanism of global topological perception in the human visual system. We showed strong evidence that the retinotectal pathway in the archicortex of the human brain is responsible for global topological perception, and for modulating the local feature processing in the classical ventral visual pathway. Inspired by this recent cognitive discovery,we developed a novel CogNet architecture to emulate the global-local dichotomy of human visual cognitive mechanisms. The thorough experimental results indicate that the proposed CogNet not only significantly improves image classification accuracies but also effectively addresses the texture bias problem observed in baseline CNN models. We have also conducted mathematical analysis for the generalization gap for general neural networks. Our theoretical derivations suggest that the Hurst parameter, a measure of the curvature of the loss landscape, can closely bind the generalization gap. A larger Hurst parameter corresponds to a better generalization ability. We found that our proposed CogNet achieves a lower test error and attains a larger Hurst parameter,strengthening its superiority over the baseline CNN models further.展开更多
文摘高频地波雷达的探测性能极易受到射频干扰的影响,当前射频干扰抑制的研究主要是通过人工识别来逐一处理,鲜见实时自动识别与抑制射频干扰的研究。随着深度学习在雷达图像处理方面应用的展开,本文尝试将其引入高频雷达射频干扰抑制中,利用YOLO(You Only Look Once)模型来识别雷达距离多普勒谱图中的射频干扰,继而用高阶奇异值分解(Higher Order Singular Value Decomposition,HOSVD)方法对其进行抑制。仿真和实测数据处理结果表明,此YOLO-HOSVD联合算法实现了对高频雷达射频干扰的自动识别与抑制,单场数据处理时间不超过1.8 s。该方法可以应用于高频地波雷达常规海态观测。
文摘为抑制海杂波检测高频地波雷达(high frequency surface wave radar,HFSWR)中的目标,提出了基于零陷展宽(null widening,NW)的空域海杂波抑制算法.NW算法利用海洋运动的连续性和海杂波的弥散性,基于干扰位置变化规律构造加权干扰NW矩阵,从阵列中抑制海杂波并保留目标.与其他海杂波抑制算法对比,利用新的波束形成方法将干扰零陷方向展宽,不改变噪声项贡献,从而有效抑制海杂波.利用NW算法在空域抑制海杂波时,根据实际应用环境计算修正矩阵,在实时信号处理过程中不仅运算速度快,而且提高了输出信号杂波噪声比(signal to clutter plus noise ratio,SCNR),针对海杂波抑制效果较好,有助于后续信号处理目标检测工作.
基金supported by the National Key Research and Development Project of China (Grant No. 2020AAA0105600)the National Natural Science Foundation of China (Grant Nos. U21B2048 and 62276208)+1 种基金Shenzhen Key Technical Projects (Grant No. CJGJZD2022051714160501)the Chinese Academy of Sciences (Grant Nos. 2021091 and YSBR-068)。
文摘In this study, we explored the neural mechanism of global topological perception in the human visual system. We showed strong evidence that the retinotectal pathway in the archicortex of the human brain is responsible for global topological perception, and for modulating the local feature processing in the classical ventral visual pathway. Inspired by this recent cognitive discovery,we developed a novel CogNet architecture to emulate the global-local dichotomy of human visual cognitive mechanisms. The thorough experimental results indicate that the proposed CogNet not only significantly improves image classification accuracies but also effectively addresses the texture bias problem observed in baseline CNN models. We have also conducted mathematical analysis for the generalization gap for general neural networks. Our theoretical derivations suggest that the Hurst parameter, a measure of the curvature of the loss landscape, can closely bind the generalization gap. A larger Hurst parameter corresponds to a better generalization ability. We found that our proposed CogNet achieves a lower test error and attains a larger Hurst parameter,strengthening its superiority over the baseline CNN models further.
