Target incremental fusion recognition method based on network ensemble

The multiple uncertainties in real radar detection scenarios require that radar automatic target recognition systems must have efficient dynamic learning capabilities. And current deep learning methods are inefficient and impractical for incremental update learning of data. To address the problem of data incremental learning in radar target recognition, a network ensemble-based target incremental fusion recognition method is proposed by combining lightweight incremental learning with neural network fusion. Multiple convolutional neural networks are constructed to be trained and tested in the split-angle domains, and then integrated and fused to obtain a robust recognition performance close to that of large data volumes with significantly reduced data requirements. The experimental results based on the measured radar data show that the proposed method can efficiently accomplish the target incremental recognition task, and has strong engineering practicality.