A Satellite Data-Driven Coherent Parameters Estimation Method via Hierarchical HGRFT for Distributed Coherent Aperture Radar

Abstract

The distributed coherent aperture radar (DCAR) utilizes full coherent processing (FCP). Compared to single radar unit observations, N radar units can achieve an $N^{3}$ times increase in signal-to-noise ratio (SNR), providing an advantage in observing distant targets. However, its stringent requirements for time and phase of multiple radar units make coherent parameters (CPs) estimation the crucial aspect of FCP. This article introduces a satellite data-driven CPs estimation method via hierarchical and hybrid generalized Radon-Fourier transform (HHGRFT). First, the FCP procedure is outlined, and the signal model with CPs is established. Second, the principles for selecting satellites and observation time in the experimental setup are induced, along with an analysis of the SNR variations during processing. Furthermore, through a hierarchical processing approach using the generalized Radon-Fourier transform (GRFT), interpulse coherence (IPC) processing, interunit-radar coherence (IURC) processing, and inter-subaperture coherent or noncoherent processing are sequentially conducted. The utilization of generalized sharpness (GS) and gradient descent method facilitated CPs estimation, subsequently enhancing the SNR post-coherence processing. Finally, the proposed method has been validated through simulation and successfully applied to a real DCAR system.