Automatic recognition of analog and digital modulation signals using DoE filter

An algorithm for recognition of analogue and digital modulation types, utilizing the decision-theoretic approach, is developed with the novel key features. The proposed 3 novel key features, the each peak number of the phase and the amplitude component obtained by the DoE (Difference of Estimator) filter and the phase gamma max, has robust properties of sensitive with modulation types and insensitive with SNR variation. This paper describes the algorithm which automatically identifies the modulation types of received signals without prior information with these some new and some old key features. The computer simulation is performed. We investigate the performance of the proposed classifier for classifying 9 modulated signals, and compare with that of the conventional decision tree classifier. Results indicated good performance (i.e. the average probability of correct classification (Pcc) of 99.5 %) at the SNR of 10 dB. Comparing with that of the conventional decision tree classifier (i.e. the average Pcc of 95.4 %), we proved that the performance of the proposed classifier is superior to that of the conventional algorithm. Specially, at the SNR as low as 7 dB, the recognition performance of digital signals shows a noticeable improvement.