Robust Multi-Target ISAR Imaging at Low SNR Based on Particle Swarm Optimization and Sequential Variational Mode Decomposition

The proliferation of Unmanned Aerial Vehicles (UAVs) poses a significant challenge for ISAR imaging. Conventional multi-target imaging methods, such as sequential CLEAN-based techniques, are often hindered by error propagation and sensitivity to noise, leading to degraded performance or even imaging failure, especially at low SNR. To address these issues, this paper proposes a novel robust imaging framework. The framework is built upon two key innovations: a partitioned block-wise compensation mechanism integrated with PSO for simultaneous and precise motion parameters estimation of multiple targets, which avoids local optima and error accumulation; and the application of Sequential Variational Mode Decomposition (SVMD) to adaptively separate and reconstruct signals, thereby suppressing inter-target aliasing and noise interference overlooked in prior studies. Simulations and measured-data experiments confirm that the proposed method maintains clear focusing and superior image quality even at low SNR, outperforming existing techniques in terms of image entropy, contrast, and resolution. This paper provides a robust and effective solution for high-resolution radar surveillance in complex multi-target scenarios.