Note: Your custom schedule will not be saved unless you create a new account or login to an existing account.
| Paper: | SAM-P7.1 |
| Session: | Applications of Multichannel Signal Processing |
| Time: | Thursday, May 20, 13:00 - 15:00 |
| Presentation: |
Poster |
| Topic: |
Sensor Array and Multichannel Signal Processing: Beamforming, direction-of-arrival estimation, and space-time adaptive processing |
| Title: |
BI-ITERATIVE LEAST SQUARE VERSUS BI-ITERATIVE SINGULAR VALUE DECOMPOSITION FOR SUBSPACE TRACKING |
| Authors: |
Shan Ouyang; University of California, Riverside | | |
| | Yingbo Hua; University of California, Riverside | | |
| Abstract: |
We first revisit the problem of optimal low-rank matrix approximation, from which a bi-iterative least square (Bi-LS) method is formulated. We then show that the Bi-LS method is a natural platform for developing subspace tracking algorithms. Comparing to the well known bi-iterative singular value decomposition (Bi-SVD) method, we demonstrate that the Bi-LS method leads to much simpler (and yet equally accurate) linear complexity algorithms for subspace tracking. This gain of simplicity is a surprising result while the reason behind it is also surprisingly simple as shown in this paper. |
| |
| Back | |