Signal structure information-based target detection with a fully convolutional network

Abstract For target echoes, some structure information can be introduced by the signal processing techniques, such as matched filtering and coherent integration, and are usually omitted in traditional target detection (TTD) methods. The detection performance is supposed to be improved with consideration of these information. To deal with the randomness in the signal structure information (SSI) induced by the sampling and uncertain distribution of scatters, we resort to the data-driven method and propose a novel detection scheme. To make use of the SSI, a fully convolutional network (FCN) is designed to hierarchically learn the SSI. Simulation results show that the better detection performance can be obtained with the proposed SSI-based target detection method comparing to the TTD method. The justifications of using the SSI and the FCN are respectively investigated by considering the oversampling strategy and a post hoc visual explanation technique. Besides, the computational complexity is partially analyzed both in theory and in experiment.