| Paper: | SAM-L2.2 | ||
| Session: | Blind Source Estimation and Channel Identification | ||
| Time: | Wednesday, May 19, 09:50 - 10:10 | ||
| Presentation: | Lecture | ||
| Topic: | Sensor Array and Multichannel Signal Processing: Source localization, separation, classification, and tracking | ||
| Title: | BLIND IDENTIFICATION OF UNDERDETERMINED MIXTURES BASED ON THE HEXACOVARIANCE | ||
| Authors: | Laurent Albera; Laboratory I3S, BP.121 | ||
| Pierre Comon; Laboratory I3S, BP.121 | |||
| Pascal Chevalier; Thales Communications | |||
| Anne Ferréol; Thales Communications | |||
| Abstract: | Static linear mixtures with more sources than sensors are considered. Blind Identification (BI) of underdetermined mixtures is addressed by taking advantage of Sixth Order (SixO) statistics and the Virtual Array (VA) concept. Surprisingly, identification methods solely based on the hexacovariance well succeed, despite their expected high estimation variance; this is due to the inherently good conditioning of the problem. A computationally simple but efficient algorithm, named BIRTH, is proposed and enables to identify the steering vectors of up to $P=N^2-N+1$ sources for arrays of $N$ sensors with space diversity only, and up to $P=N^2$ for those with angular and polarization diversities. Five numerical algorithms are compared. | ||
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