M-FCN based sea-surface weak target detection  

作　　者：PAN Meiyan SUN Jun YANG Yuhao LI Dasheng YU Junpeng 

机构地区：[1]The 14th Research Institute of China Electronics Technology Group Corporation,Nanjing 210039,China [2]Key Laboratory of IntelliSense Technology,China Electronics Technology Group Corporation,Nanjing 210039,China

出　　处：《Journal of Systems Engineering and Electronics》2021年第5期1111-1118,共8页系统工程与电子技术（英文版）

基　　金：This was work supported by the National Natural Science Foundation of China(U19B2031).

摘　　要：This paper focuses on the sea-surface weak target detection based on memory-fully convolutional network(M-FCN)in strong sea clutter.Firstly,the constant false alarm rate(CFAR)detection method utilizes a low threshold with high probability of false alarm to detect sea-surface weak targets after non-coherent integration.Reducing the detection threshold can generate a large number of false alarms while increasing the detection rate,and how to suppress a large number of false alarms is the key to improve the performance of weak target detection.Then,the detection result of the low threshold is operated to construct the target matrix suitable for the size of fully convolutional networks and the convolution operator form.Finally,the M-FCN architecture is designed to learn the different accumulation characteristics of the target and the sea clutter between different frames.For improving the detection performance,the historical multi-frame information is memorized by the network,and the end-to-end structure is established to detect sea-surface weak target automatically.Experimental results on measured data demonstrate that the M-FCN method outperforms the traditional track before detection(TBD)method and reduces false alarm tracks by 35.1%,which greatly improves the track quality.

关 键 词：sea-surface weak target detection memory-fully convolutional network(M-FCN) multi-frame information END-TO-END 

分 类 号：TN957.51[电子电信—信号与信息处理] TP183[电子电信—信息与通信工程]