| Summary: | In this paper, a deep neural network with concatenated structure is created for the recognition of flight targets. Compared with the traditional recognition method, the deep network model automatically gets deeper structure information that is more useful for the classification, and the better performance of target recognition is also obtained when using high-resolution range profile for radar automatic target recognition. First, the framework is expanded and cascaded by multiple shallow neural sub-networks. Then, a secondary-label coding method is proposed to solve the target-aspect angle sensitivity problem. The samples are divided into sub-classes based on aspect angle, each of which is assigned a separate encoding bit in category label. Finally, the recognition results of multiple samples are fused by a multi-evidence fusion strategy for the improvement of recognition rate. Furthermore, the effectiveness of the proposed algorithm is demonstrated on the measured and simulated data.
|
|---|
