  Nowadays, target recognition technique plays an important role in many
fields. However, the existing image information based methods suffer from the
influence of target image quality. In addition, some methods also need image
reconstruction, which will bring additional time cost. In this paper, we
propose a novel coincidence recognition method combining ghost imaging (GI) and
generative adversarial networks (GAN). Based on the mechanism of GI, a set of
random speckles sequence is employed to illuminate target, and a bucket
detector without resolution is utilized to receive echo signal. The bucket
signal sequence formed after continuous detections is constructed into a bucket
signal array, which is regarded as the sample of GAN. Then, conditional GAN is
used to map bucket signal array and target category. In practical application,
the speckles sequence in training step is still employed to illuminate target,
and the bucket signal array is input GAN for recognition. The proposed method
can improve the problems caused by existing recognition methods that based on
image information, and provide a certain turbulence-free ability. Extensive
experiments are show that the proposed method achieves promising performance.
