2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
Login Paper Search My Schedule Paper Index Help

My ICASSP 2021 Schedule

Note: Your custom schedule will not be saved unless you create a new account or login to an existing account.
  1. Create a login based on your email (takes less than one minute)
  2. Perform 'Paper Search'
  3. Select papers that you desire to save in your personalized schedule
  4. Click on 'My Schedule' to see the current list of selected papers
  5. Click on 'Printable Version' to create a separate window suitable for printing (the header and menu will appear, but will not actually print)

Paper Detail

Paper IDSAM-1.4
Paper Title NON-COHERENT DOA ESTIMATION OF OFF-GRID SIGNALS WITH UNIFORM CIRCULAR ARRAYS
Authors Zhengyu Wan, Wei Liu, University of Sheffield, United Kingdom
SessionSAM-1: Direction of Arrival Estimation 1
LocationGather.Town
Session Time:Tuesday, 08 June, 16:30 - 17:15
Presentation Time:Tuesday, 08 June, 16:30 - 17:15
Presentation Poster
Topic Sensor Array and Multichannel Signal Processing: [SAM-DOAE] Direction of arrival estimation and source localization
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Recently, some non-coherent DOA estimation methods are presented under a sparse phase retrieval framework, where DOAs of incident signals are assumed to be on the predefined grid points. However, this may not be correct in practice; in order to address this issue, an off-grid model involved with a bias vector is proposed and an efficient two-step method based on this model is developed. In addition, instead of using ULAs, uniform circular arrays (UCAs) are employed in order to overcome the ambiguities arising in non-coherent measurements, as analysed in detail. Numerical simulations show that, compared to on-grid model with a denser grid points, the off-grid model with a coarse grid can achieve a better performance with a lower computational complexity.