During a sea firing training,the intelligent detection of projectile-induced water column targets in a firing video is the prerequisite for and critical to the automatic calculation of miss distance,while the correct ...During a sea firing training,the intelligent detection of projectile-induced water column targets in a firing video is the prerequisite for and critical to the automatic calculation of miss distance,while the correct and precise calculation of miss distance is directly affected by the accuracy,false alarm rate and time delay of detection.After analyzing the characteristics of projectile-induced water columns,an accurate detection algorithm for time backtracked projectile-induced water columns based on the improved you only look once(YOLO)network is put forward.The capability and accuracy of detecting projectileinduced water column targets with the conventional YOLO network are improved by optimizing the anchor box through K-means clustering and embedding the squeeze and excitation(SE)attention module.The detection area is limited by adopting a sea-sky line detection algorithm based on gray level co-occurrence matrix(GLCM),so as to effectively eliminate such disturbances as ocean waves and ship wakes,and lower the false alarm rate of projectile-induced water column detection.The improved algorithm increases the mAP50 of water column detection by 30.3%.On the basis of correct detection,a time backtracking algorithm is designed with mean shift to track images containing projectile-induced water column in reverse time sequence.It accurately detects a projectile-induced water column at the time of its initial appearance as well as its pixel position in images,and considerably reduces detection delay,so as to provide the support for the automatic,accurate,and real-time calculation of miss distance.展开更多
针对低信噪比(signal to noise ratio,SNR)低截获概率(low probability of intercept,LPI)雷达脉内波形识别准确率低的问题,提出一种基于时频分析、压缩激励(squeeze excitation,SE)和ResNeXt网络的雷达辐射源信号识别方法。首先通过Cho...针对低信噪比(signal to noise ratio,SNR)低截获概率(low probability of intercept,LPI)雷达脉内波形识别准确率低的问题,提出一种基于时频分析、压缩激励(squeeze excitation,SE)和ResNeXt网络的雷达辐射源信号识别方法。首先通过Choi-Williams分布(Choi-Williams distribution,CWD)获得雷达时域信号的二维时频图像(time-frequency image,TFI);然后进行TFI预处理降低噪声干扰和频率维的位置分布差异,以适应深度学习网络输入;最后在ResNeXt基础上加入扩张卷积和SE结构提取TFI特征,实现雷达辐射源分类。实验结果表明,SNR低至-8 dB时,该方法对12类常见LPI雷达波形的整体识别准确率依然能达到98.08%。展开更多
基金supported by the National Natural Science Foundation of China(51679247)。
文摘During a sea firing training,the intelligent detection of projectile-induced water column targets in a firing video is the prerequisite for and critical to the automatic calculation of miss distance,while the correct and precise calculation of miss distance is directly affected by the accuracy,false alarm rate and time delay of detection.After analyzing the characteristics of projectile-induced water columns,an accurate detection algorithm for time backtracked projectile-induced water columns based on the improved you only look once(YOLO)network is put forward.The capability and accuracy of detecting projectileinduced water column targets with the conventional YOLO network are improved by optimizing the anchor box through K-means clustering and embedding the squeeze and excitation(SE)attention module.The detection area is limited by adopting a sea-sky line detection algorithm based on gray level co-occurrence matrix(GLCM),so as to effectively eliminate such disturbances as ocean waves and ship wakes,and lower the false alarm rate of projectile-induced water column detection.The improved algorithm increases the mAP50 of water column detection by 30.3%.On the basis of correct detection,a time backtracking algorithm is designed with mean shift to track images containing projectile-induced water column in reverse time sequence.It accurately detects a projectile-induced water column at the time of its initial appearance as well as its pixel position in images,and considerably reduces detection delay,so as to provide the support for the automatic,accurate,and real-time calculation of miss distance.
文摘针对低信噪比(signal to noise ratio,SNR)低截获概率(low probability of intercept,LPI)雷达脉内波形识别准确率低的问题,提出一种基于时频分析、压缩激励(squeeze excitation,SE)和ResNeXt网络的雷达辐射源信号识别方法。首先通过Choi-Williams分布(Choi-Williams distribution,CWD)获得雷达时域信号的二维时频图像(time-frequency image,TFI);然后进行TFI预处理降低噪声干扰和频率维的位置分布差异,以适应深度学习网络输入;最后在ResNeXt基础上加入扩张卷积和SE结构提取TFI特征,实现雷达辐射源分类。实验结果表明,SNR低至-8 dB时,该方法对12类常见LPI雷达波形的整体识别准确率依然能达到98.08%。
