End-to-End Recognition of Similar Space Cone-Cylinder Targets Based on Complex-Valued Coordinate Attention Networks

Except for the slight difference of micromotion parameters, some decoys and warheads have the same geometry and micromotion form. As a result, recognition of similar space cone-cylinder targets is one of the difficult problems in ballistic target recognition. In recent years, due to the good effect of deep neural networks (DNNs) in optical target recognition, space cone-cylinder target recognition methods based on DNN have attracted wide attention. However, these DNN-based methods only recognize the space cone-cylinder targets with different shapes and different micromotion forms. Moreover, these methods require some time-consuming preprocessing operations, which need to observe the target for at least one micromotion period. To recognize similar space cone-cylinder targets, we propose a complex-valued coordinate attention networks (CV-CANets)-based end-to-end recognition method. Firstly, we establish the signal model of space cone-cylinder targets. Secondly, we propose CV-CA blocks by transforming the coordinate attention mechanism into the complex-valued domain. Then, we construct CV-CANet based on the proposed CV-CA blocks. Finally, the proposed CV-CANet is trained and tested by the narrowband radar echo data, which is generated by electromagnetic calculation. Compared with the convolutional neural network (CNN)-based recognition methods, the proposed method can not only recognize the similar space cone-cylinder targets but also is superior in terms of time cost and observation requirement. Extensive experiments validate that the proposed recognition method is effective when the targets only have a slight difference on the precession angular frequency and the observation time is less than half a period.