Parameter Estimation of Frequency-Hopping Signal in UCA Based on Deep Learning and Spatial Time–Frequency Distribution

The parameter estimation and sorting of frequency-hopping (FH) signals are of great importance in both civilian and military applications. In this article, the estimation of hopping time, hopping period, hopping frequency, instantaneous bandwidth, and 2-D direction of arrival (2-D-DOA) of multiple FH signals in the uniform circular array (UCA) is studied. First, a novel lightweight model based on You Only Look Once (YOLO), named FH-GYOLO, is proposed to detect FH signals and estimate their time–frequency (TF) parameters. The proposed FH-GYOLO takes advantage of the ghost module, which can significantly reduce the computational cost and, simultaneously, achieve an efficient detection with high performance. Then, the 2-D-DOA estimation method of FH signals in UCA is proposed with the spatial TF distribution (STFD). In the proposed method, the steering vector of UCA and the STFD matrix are transformed into beamspace by beamspace transformation, and the closed-form solution is derived for the estimation of autopaired elevation and azimuth angles without the computationally expensive spatial–spectral search. Furthermore, in the proposed method, an iterative compensation process is developed, which can greatly improve the accuracy of DOA estimation without significantly increasing the computational complexity. Extensive simulation results and analysis demonstrated that the proposed algorithms exhibit better or similar performance than the other benchmark methods, and most importantly, the computational cost is substantially reduced.