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About

Zonglong Bai is a researcher whose work sits at the intersection of sparse signal processing, Bayesian inference, and acoustic sensing—with a particular focus on enabling robots and drones to "hear" their environments with high precision. His core research addresses the fundamental challenge of estimating the direction-of-arrival (DOA) of multiple acoustic sources, a critical capability for humanoid robots and autonomous drones operating in constrained, real-world settings. Bai’s most cited work, “Acoustic DOA estimation using space alternating sparse Bayesian learning” (2021, 22 citations), introduces a powerful framework that overcomes the limitations of small array apertures typical on compact robotic platforms. He has further advanced the field by pioneering the use of ℓ1/2-norm regularization within sparse Bayesian learning (2023, 17 citations), offering a more robust approach to sparse signal recovery. His earlier work on reconstructing room impulse responses (2019) provides a complete pipeline for joint time-of-arrival and DOA estimation, directly tackling the problem of acoustic reflector mapping for robot audition. With a growing citation record and a clear trajectory from theoretical Bayesian methods to practical acoustic navigation, Bai is establishing himself as a key contributor to the next generation of intelligent, hearing-enabled autonomous systems.