This paper addresses the problem of detection, imaging and automatic target recognition of moving targets using synthetic aperture radar. The challenge in processing moving targets is addressed with special emphasis on the blind angle ambiguity and on the maximum unambiguous moving target velocity. It is shown that both ambiguities can be solved by including information about the antenna radiat...

In this paper, we present a novel synthesis of two separate areas of image processing: automatic target recognition/cueing (ATR/ATC) and embedded image compression. In order to maximize the information content of a transmitted image, an ATR algorithm is used to detect potential areas of interest, and the compression algorithm regionally compresses the image, allocating more bits to the areas of...

This effort advanced the art of applying Grenander's pattern theory to automatic target recognition (ATR) problems. We extended jump-diffusion ATR algorithms to accommodate unknown infrared camera calibration effects and include more stable diffusion procedures for pose refinement, and developed flexible shape models to accommodate clutter. We also developed performance bounds on estimation and...

Deep learning based synthetic aperture radar automatic target recognition (SAR-ATR) plays an significant role in the military and civilian fields. However, data limitation large computational cost are still severe challenges actual application of SAR-ATR. To improve performance CNN model with limited samples SAR-ATR, this paper proposes a novel multi-domain feature subspaces fusion representati...

| This paper describes techniques to perform ee-cient and accurate target recognition in diicult domains. In order to accurately model small, irregularly shaped targets, the target objects and images are represented by their edge maps, with a local orientation associated with each edge pixel. Three-dimensional objects are modeled by a set of two-dimensional views of the object. Translation, rot...

Automatic target recognition that relies on rapid feature extraction of real-time target from photo-realistic imaging will enable efficient identification of target patterns. To achieve this objective, Cross-plots of binary patterns are explored as potential signatures for the observed target by high-speed capture of the crucial spatial features using minimal computational resources. Target rec...

This paper describes a novel approach to automatically recognize the target based on a view morphing database constructed by our multi-view morphing algorithm. Instead of using single reference image, a set of images or a video sequence is used to construct the reference database, where these images are re-organized by a triangulation of viewing sphere. At the vertex of each triangle, one image...

Underwater acoustic target recognition (UATR) technology based on deep learning and automatic encoding has become an important research direction in the underwater field recent years. However, existing methods do not have favourable self-adaptability for different data because of complex changeable environment, which easily leads to unsatisfactory effect. The concept contrastive is introduced i...

This paper explores statistically pose estimation in SAR ATR. Based on our proposed method of maximizing mutual information, further experiments are conducted by using the new MSTAR/ IU Database. Different pose estimator topologies and training criteria are also employed. Experimental results show that our proposed method reduces the average pose estimation error to within 10 degrees of the tru...