| Paper: | SAM-P2.1 | ||
| Session: | Detection and Estimation | ||
| Time: | Tuesday, May 18, 15:30 - 17:30 | ||
| Presentation: | Poster | ||
| Topic: | Sensor Array and Multichannel Signal Processing: Signal detection and estimation | ||
| Title: | NON EFFICIENCY AND NON GAUSSIANITY OF A MAXIMUM LIKELIHOOD ESTIMATOR AT HIGH SIGNAL TO NOISE RATIO AND FINITE NUMBER OF SAMPLES | ||
| Authors: | Alexandre Renaux; École Normale Supérieuré de Cachan | ||
| Philippe Forster; IUT de Ville d'Avray | |||
| Eric Boyer; École Normale Supérieuré de Cachan | |||
| Pascal Larzabal; École Normale Supérieuré de Cachan | |||
| Abstract: | In estimation theory, the asymptotic efficiency of the Maximum Likelihood (ML) method for independent identically distributed observations and when the number T of observations tends to infinity is a well known result. In some scenarii, the number of snapshots may be small making this result unapplicable. In the array processing framework, for Gaussian emitted signals, we fill this lack at high Signal to Noise Ratio (SNR). In this situation, we show that the ML estimation is asymptotically (with respect to SNR) non efficient and non Gaussian. | ||
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