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 IDSPTM-3.3
Paper Title A NEW DIFFUSION VARIABLE SPATIAL REGULARIZED QRRLS ALGORITHM
Authors Yijing Chu, South China University of Technology, China; S. C. Chan, University of Hong Kong, China; Yi Zhou, Chongqing University of Posts and Telecommunications, China; Ming Wu, Institute of Acoustics, China
SessionSPTM-3: Estimation, Detection and Learning over Networks 1
LocationGather.Town
Session Time:Tuesday, 08 June, 14:00 - 14:45
Presentation Time:Tuesday, 08 June, 14:00 - 14:45
Presentation Poster
Topic Signal Processing Theory and Methods: [ASP] Adaptive Signal Processing
Abstract This paper develops a framework for the design of diffusion adaptive algorithms, where a network of nodes aim to estimate system parameters from the collected distinct local data stream. We explore the time and spatial knowledge of system responses and model their evolution in both time and spatial domain. A weighted maximum a posteriori probability (MAP) is used to derive an adaptive estimator, where recent data has more influence on statistics via weighting factors. The resulting recursive least squares (RLS) local estimate can be implemented by the QR decomposition (QRD). To mediate the distinct spatial information incorporation within neighboring estimates, a variable spatial regularization (VSR) parameter is introduced. The estimation bias and variance of the proposed algorithm are analyzed. A new diffusion VSR QRRLS (Diff-VSR-QRRLS) algorithm is derived that balances the bias and variance terms. Simulations are carried out to illustrate the effectiveness of the theoretical analysis and evaluate the performance of the proposed algorithm.