  Automatic target recognition (ATR) based on inverse synthetic aperture radar
(ISAR) images, which is extensively utilized to surveil environment in military
and civil fields, must be high-precision and reliable. Photonic technologies'
advantage of broad bandwidth enables ISAR systems to realize high-resolution
imaging, which is in favor of achieving high-performance ATR. Deep learning
(DL) algorithms have achieved excellent recognition accuracies. However, the
lack of interpretability of DL algorithms causes the head-scratching problem of
credibility. In this paper, we exploit the inner relationship between a
photonic ISAR imaging system and behaviors of a convolutional neural network
(CNN) to deeply comprehend the intelligent recognition. Specifically, we
manipulate imaging physical process and analyze network outputs, the relevance
between the ISAR image and network output, and the visualization of features in
the network output layer. Consequently, the broader imaging bandwidths and
appropriate imaging angles lead to more detailed structural and contour
features and the bigger discrepancy among ISAR images of different targets,
which contributes to the CNN recognizing and distinguishing objects according
to physical laws. Then, based on the photonic ISAR imaging system and the
explainable CNN, we accomplish a high-accuracy and reliable ATR. To the best of
our knowledge, there is no precedent of explaining the DL algorithms by
exploring the influence of the physical process of data generation on network
behaviors. It is anticipated that this work can not only inspire the
accomplishment of a high-performance ATR but also bring new insights to explore
network behaviors and thus achieve better intelligent abilities.
