220 / 2020-01-02 09:42:00
Deep Learning Based Broadband DOA Estimation
Draft Rejected
Yi Ma / Shenzhen University, China
Jinfeng Zhang / Shenzhen University, China
Ping Chu / Shenzhen University, China
Bin Liao / Shenzhen University, China
This paper proposes a fast learning-based method for direction-of-arrival (DOA) estimation of multiple broadband far-field sources. The processing procedure involves two steps. First, a beamspace preprocessing which has the property of frequency invariant is applied to the array outputs to perform focusing over a wide bandwidth. By converting the outputs from the element-space to beamspace in this step, the computation can be reduced through adjusting the number of beamformers. In the second step, a hierarchical deep neural network is employed to achieve classification, which can output the DOA estimates. Simulation results verify the effectiveness of the proposed method.
Important Date
  • Conference Date

    Jun 08

    2020

    to

    Jun 11

    2020

  • Jan 12 2020

    Draft paper submission deadline

  • Apr 15 2020

    Early Bird Registration

  • Dec 31 2020

    Registration deadline

Sponsored By
IEEE Signal Processing Society
Organized By
Zhejiang University
Contact Information