| 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. | ||
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