Highly Maneuvering Target Detection Based on Neural Network and Generalized Radon-Fourier Transform

Detection of highly maneuvering targets often suffers from the problem of range migration (RM) and Doppler frequency migration (DFM) within coherent processing interval (CPI), which results in performance degradation in coherent integration. In this letter, we propose a new coherent integration method which is based on neural networks and generalized Radon-Fourier transform (GRFT). Specifically, this method develops a neural network that can infer the target trajectory from the radar echo, and the trajectory reduces the search ranges of the GRFT method’s parameters according to some judgment criteria. After that, the RM and DFM are compensated via GRFT, and thereby it improves the performance of coherent integration. Besides, we introduce a customized regularization loss into the development of the neural network, which improves the detection performance. The superiority of the proposed method over GRFT is that it reduces the computational cost significantly with similar detection performance, which is confirmed by the results of experiments.