Variable Length Sequential Iterable Convolutional Recurrent Network for UWB-IR Vehicle Target Recognition

A variable length sequential iterable convolutional recurrent network (VS-ICRN) is proposed in this article, aiming at improving the vehicle target recognition ability for the ultrawideband impulse radar (UWB-IR). First, the array imaging technology is introduced into the UWB-IR, and thus, a range-angle imaging method for the array UWB-IR is put forward, to simulate the array UWB-IR vehicles image under different observation conditions. Second, in order to make full use of both the deep features in the single image and the deep associated features between the sequence images, a VS-ICRN model is proposed, which includes three submodules: the image feature extraction based on the iterable convolution, the variable length sequential image associated feature extraction, and the target classification. Finally, the experiment on the simulation dataset and the Moving and Stationary Target Acquisition and Recognition (MSTAR) is carried out to validate the effectiveness of the proposed method. The experimental results on the simulation dataset show that when SNR
$= -10$
dB, the proposed method is superior in the recognition rate to the GoogLeNet and AlexNet methods with 16% and 19%, respectively. Meanwhile, the proposed VS-ICRN method only needs 1.38% parameters quantity to achieve a comparable recognition rate as GoogLeNet on the MSTAR dataset.