M1.R1.1
Fast and Interpretable Sparse Array Reconstruction via Broad Neural Networks for Automotive Radar
Lifan Xu, Yunqiao Hu, Shunqiao Sun, The University of Alabama, United States; Ryan Wu, Binbin Shi, Jun Li, NXP Semiconductors, United States
Session:
M1.R1: Sparse Modeling and Learning Methods Lecture
Track:
Sensor Array and Multichannel Signal Processing
Location:
Isabela I
Presentation Time:
Mon, 15 Dec, 10:20 - 10:40 AST (UTC -4)
Session Co-Chairs:
Shunqiao Sun, The University of Alabama and Henry Arguello, Universidad Industrial de Santander
Session M1.R1
M1.R1.1: Fast and Interpretable Sparse Array Reconstruction via Broad Neural Networks for Automotive Radar
Lifan Xu, Yunqiao Hu, Shunqiao Sun, The University of Alabama, United States; Ryan Wu, Binbin Shi, Jun Li, NXP Semiconductors, United States
M1.R1.2: OPTIMIZED SPARSE NETWORK COVERAGE VIA L1-NORM MINIMIZATION
Souvik Paul, Lenovo Deutchland GmbH, Germany; Ivan Alexander Morales Sandoval, Giuseppe Thadeu Freitas de Abreu, Constructor University, Germany
M1.R1.3: A PRECONDITIONED HESSIAN-LIKE PROXIMAL GRADIENT ALGORITHM FOR SPARSE ML-DOA ESTIMATION
Thomas Aussaguès, Anne Ferréol, Alice Delmer, THALES, France; Pascal Larzabal, SATIE, France
M1.R1.4: 3D SEISMIC ACQUISITION DESIGN OPTIMIZATION REGULARIZED BY FWI: A DEEP LEARNING APPROACH
Ana Mantilla-Dulcey, Javier Torres-Quintero, Henry Arguello, Universidad Industrial de Santander, Colombia
M1.R1.5: MULTI-AGENT ADAPTIVE SUPER-RESOLUTION SPARSE SOURCE LOCALIZATION FOR ADVECTION-DIFFUSION PROCESSES
Dmitriy Shutin, Victor Scott Prieto Ruiz, Patrick Hinsen, Thomas Wiedemann, German Aerospace Center (DLR), Germany
Contacts