Severe phenomena of across range-Doppler unit (ARDU) and decoherence occur when radar detects high-speed and high-maneuvering targets, causing degradation in detection performance of traditional FFT radar detection methods. The improvement in radar resolution causes a multi-dimensional spread phenomenon, where different scattering centers of the target are distributed on different range units, along with motion parameters such as velocity and acceleration. Unfortunately, current radar detection methods focus solely on range spread targets and cannot handle multi-dimensional spread, leading to a significant decline in detection performance. To overcome this problem, this paper proposes several methods to achieve high detection performance for multi-dimensional spread target detection with ARDU phenomenon. Firstly, the generalized likelihood ratio test (GLRT) is derived, and the energy integration generalized Rayleigh Fourier transform (EI-GRFT) is introduced to improve the detection performance of range spread cross-unit targets. Additionally, the double-threshold based hybrid GRFT (DT-HGRFT) is presented as an enhancement of EI-GRFT, enabling long-time integration along slow time and integration among multiple scatters by using HGRFT and multi-dimensional sliding double-threshold detection, respectively. Furthermore, a method for joint detections of multiple DT-HGRFTs is provided to handle the case where the number of scattering centers of multi-dimensional spread targets is unknown. Finally, a detailed theoretical analysis of the performance of the proposed method is presented, along with extensive simulations and practical experiments to demonstrate the effectiveness of the proposed methods.

Three-dimensional (3-D) inverse synthetic aperture radar (ISAR) imaging technology is widely used for noncooperative targets, which can obtain precise topography, structure and rotation information of celestial target. However, celestial target observation has the features of long observation distance, low echo signal-to-noise ratio (SNR) and complex rotation characteristic, which severely degrade the performance of traditional 3-D ISAR methods. Therefore, in order to realize high-precision 3-D ISAR imaging of celestial target, a parametric 3-D ISAR imaging method is proposed in this paper. Firstly, a 3-D imaging model is established based on the complex rotation characteristic of celestial target, which indicates that the rotation vector and polar diameter estimation is the key of 3-D reconstruction. Secondly, in order to overcome the performance degradation of traditional 3-D ISAR methods under low SNR, a parameter estimation method based on hybrid generalized radon-Fourier transform (HGRFT) is proposed, and the core is to use GRFT within sub-aperture and non-coherent accumulation between sub-apertures to achieve hybrid accumulation of echo signals, so as to obtain the desired rotation vector and polar diameter fast and accurately. Consequently, the 3-D reconstruction of celestial target under low SNR can be achieved based on the 3-D imaging model and the parameter estimation results. Moreover, two fast implementations based on parameter search space dimensionality reduction and heuristic search, respectively, are proposed, which can reduce the computational load of high-dimensional space parameter estimation and further improve the algorithm efficiency. Finally, the proposed method is validated by celestial target 3-D ISAR imaging simulation.

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. The paper introduces a satellite data-driven CPs estimation method via hierarchical and hybrid generalized Radon-Fourier transform (HHGRFT). Firstly, the FCP procedure is outlined, and the signal model with CPs is established. Secondly, 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), inter-pulse coherence processing, inter-unit-radar coherence processing, and inter-sub-aperture coherent or non-coherent processing are sequentially conducted. The utilization of generalized sharpness 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.