Liming Shi

Papers

Total Citations

H-Index

About

Liming Shi is a researcher whose work lies at the intersection of acoustic signal processing, array processing, and machine learning, with a particular focus on direction-of-arrival (DOA) estimation. His most cited paper, "Acoustic DOA estimation using space alternating sparse Bayesian learning" (2021, 22 citations), addresses a critical challenge in humanoid robotics and drone technology: accurately localizing sound sources using compact microphone arrays. Shi’s key contribution is the development of a sparse Bayesian learning framework that efficiently estimates DOA even under the severe aperture constraints imposed by small platforms. This work not only advances theoretical understanding of sparse signal recovery but also has direct practical implications for enabling robots and drones to perceive their auditory environment—a capability essential for human-robot interaction and autonomous navigation. By tackling the trade-off between array size and estimation accuracy, Shi’s research demonstrates how sophisticated algorithmic approaches can overcome hardware limitations. His contributions are particularly valuable for students and researchers working on sensor arrays, autonomous systems, and acoustic scene analysis, offering a compelling example of how Bayesian methods can solve real-world engineering problems.