Jesper Rindom Jensen
Papers
7
Total Citations
80
H-Index
6
About
Jesper Rindom Jensen is a leading researcher in robot and drone audition, specializing in acoustic signal processing for autonomous platforms. His work centers on three key areas: direction-of-arrival (DOA) estimation, acoustic echo analysis, and spatial mapping for robotics. Jensen’s most impactful contribution is his development of a space alternating sparse Bayesian learning framework for acoustic DOA estimation (2021, 22 citations), which addresses the critical challenge of limited array aperture on compact robotic platforms. He has pioneered model-based approaches for acoustic reflector localization, enabling robots to detect transparent surfaces like glass that traditional camera- and laser-based systems cannot perceive. His innovative framework for spatial map generation using acoustic echoes (2022, 17 citations) combines nonlinear least squares estimation with beamforming to construct indoor environment maps. Jensen has also advanced expectation-maximization methods for multichannel time-of-arrival and DOA estimation of echoes (2019-2020), and introduced a novel technique using a robot’s own ego-noise to detect nearby acoustic reflectors (2021). His work is fundamental to enabling autonomous navigation and environmental awareness for robots and drones in complex indoor settings.
Research Focus
Key Achievements
Top Papers
- 1Acoustic DOA estimation using space alternating sparse Bayesian learning22 citations · 2021
- 2
- 3Estimation of acoustic echoes using expectation-maximization methods14 citations · 2020
- 4An Em Method for Multichannel Toa and Doa Estimation of Acoustic Echoes11 citations · 2019
- 5
- 6
- 7Detecting Acoustic Reflectors Using A Robot’s Ego-Noise3 citations · 2021