A GRFT-Like Method for Highly Maneuvering Target Detection via Neural Network

Range migration (RM) and Doppler frequency migration (DFM) make it challenging to achieve coherent integration for highly maneuvering targets. Generalized radon-Fourier transform (GRFT) can compensate for RM and DFM, enabling coherent integration for targets, but its computational cost is significant. To address the aforementioned issue, this letter proposes a novel coherent integration method based on neural networks and GRFT. The proposed method utilizes a neural network to infer target trajectories from radar echoes. It narrows down the motion parameter search range of the GRFT method based on the inferred target trajectories, completing the coherent integration of target energy. Compared to GRFT and other existing methods, the proposed method achieves a better balance between detection performance and computational cost. At the same time, the proposed method possesses a certain degree of generalizability, maintaining good performance across different radar parameters. The results of simulation experiments confirm the aforementioned advantages.