Auto-Modulation Identification Algorithm Of Digital Signals Based On Wavelet Theory

Automatic identification of communicate signal modulation type has the greatsignificance to the electronic reconnaissance receiver, which is an important aspect ofelectronic countermine. Recognise the modulation type correctly, we can carry oninterception or disturbance to the enemy better, constitute defenses and attacksstrategy more pertinently.This paper mainly deals with the wavelet algorithm in automatic identification ofdigital communication signal modulation types. The characters of the digital signal indifferent modulation types in wavelet transform domain are extracted, and atrapezoidal sorter is designed, the characters in the wavelet domain being used asmain identification parametres, automatic identification model of the digital signalmodulation type using wavelet algorithm is established. Massive simulations haveconfirmed good performance of this algorithm. The correct identification rate of mostdigital signal has all achieved above 90％when SNR＞5db, which satisfies the requestof this project well. The modulation parameters of digital signal are also estimatedwith the wavelet in this paper. In order to extract the signal character and identify themodulation type more correctly, the author has proved some related equations of thealgorithm.The main research achievements and innovations of this paper as follows:1. Several kinds of digital communication signals which are requested by theproject are identified. The characters of digital modulated signal in wavelet domainand time domain are combined, the absolute amplitude variance of digital modulatedsignal, the amplitude and phase-difference of wavelet transformation coefficients areextracted. This identification algorithm is very simple, and it has strong robustness.The characteristic parameters are few, and the recognition time is short.2. The characters of the OQPSK signal in wavelet domain are studied.3. The influence of the histogram statistics sector numbers on the judgement ofthe number of step layers is discussed in the identification of MFSK, MASK andMPSK.The influence is reduced by the multi-sectors histogram statistics. 4. The influence of scale on the signal characteristic extraction is deeply studiedin this paper. Different scales are selected according to different need, in order toreduce the influence of noise. The phase-difference information of wavelet coefficientunder several scales between the neighboring symbols is extracted when the MPSKsignals are distinguished.5. The modulation parameters of digital signal are estimated with the wavelet. Anew method to estimate the symbol cycle of MASK signal is proposed. This methodis a very good reference to other digital signal elements estimated, and its versatilityis very strong.