Full-Dimensional Partial-Search Generalized Radon-Fourier Transform for High-Speed Maneuvering Target Detection

Long time coherent integration for high-speed maneuvering targets in radar signal processing poses substantial difficulties due to the occurrence of Range Migration (RM) and Doppler Frequency Migration (DFM). The Generalized Radon-Fourier Transform (GRFT), an effective method for accumulating target energy, is hampered by its high computational cost. To address this, the Full-Dimensional Partial-Search Generalized Radon-Fourier Transform (FSGRFT), a fast implementation of the GRFT algorithm, is proposed in this paper. Differing from the exhaustive full-dimensional search of the GRFT algorithm, the FSGRFT algorithm first employs a pre-trained residual network to coarse estimate range cells where targets might exist, along with their corresponding motion parameter sub-spaces. Guided by this initial coarse estimate, the FSGRFT algorithm narrows its search to a subset of range cells and the designated sub-space of motion parameters, thereby considerably decreasing the number of searches. Meanwhile, the introduction of a lightweight network structure with robust representation capabilities ensures the accuracy of the coarse estimation, without bringing excessive computational cost. Therefore, the FSGRFT algorithm obtains a good trade-off between the computational cost and integration performance, in comparison with other long-time coherent integration algorithms. Finally, simulation results demonstrate that the FSGRFT algorithm can achieve detection performance comparable to the GRFT algorithm, but with considerably lower computational cost.