Sparse Blind Deconvolution Using ADMM Methods Based on Asymmetric Structured Prior for UWB Fuze

The precise ranging of ultra-wideband (UWB) fuzes relies on extracting time delay information from echo signals. However, ground multipath propagation effects induce a significant time-delay spread in the echo signals. This manifests as a channel impulse response (CIR) composed of numerous, closely spaced components, creating a challenging super-resolution problem that severely constrains the ranging accuracy and reliability of the fuze. Therefore, accurately estimating the CIR that characterizes these multipath structures from a single echo observation is crucial for the UWB fuze to perceive terrain structures and enhance ranging capabilities. This study proposes the following methods: (1) establishing an equivalent discrete multipath model(EDMM) of the ground to characterize the CIR; (2) proposing a sparse blind deconvolution(SBD) method via the ADMM-based framework under an asymmetric structured prior (ASP), which employs parametric projections to constrain the physical morphology of the unknown source signal, and designing a periodic sparse cluster projection operator to achieve super-resolution recovery of the discrete multipath structure of the channel h by enforcing the EDMM prior. Through three-variable robust decomposition, it actively separates dispersed clutter and enhances performance under low signal-to-noise ratio (SNR) conditions. Experimental results from both simulations and measured data demonstrate that the proposed algorithm exhibits excellent robustness and recovery accuracy in complex low-SNR scenarios, providing a foundational offline analysis method for understanding complex channel characteristics and guiding the development of improved real-time ranging algorithms.