Abstract
This paper first discusses the maximum likelihood (ML) classifier for automatic classification of digital modulations. The classifier is optimum for classification of phase-amplitude modulated signals under ideal environment. However, this is not the case in the presence of phase offset owing to inaccurate estimation. In this paper, we propose a novel non-coherent ML classifier to mitigate the effect phase offset. The non-coherent ML classifier adopts a pre-classification phase correction stage through a closed form estimator based on Higher Order Statistics. Experimental results show improvement of classification accuracy at reasonable signal to noise ratio.
Original language | English |
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Title of host publication | 18th International Conference on Pattern Recognition |
Pages | 177-180 |
Number of pages | 4 |
DOIs | |
Publication status | Published - 2006 |
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Computer Vision and Pattern Recognition
- Hardware and Architecture