# Modulation Recognition System of Electromagnetic Interference Signal Based on SDR
## 1 Introduction  
In the field of non-cooperative communication, modulation recognition means that the receiver automatically recognizes the modulation type of the signal without prior knowledge and noise interference. Combining software defined radio, which, as a multifunctional radio communication platform with modulation recognition technology, can effectively realize the automatic reception and recognition of communication signals, and improve the universality and practicability of modulation recognition [1; 2].  
At present, the methods of modulation recognition mainly include maximum likelihood based on hypothesis testing and pattern recognition based on feature extraction. [PERSON] [3], [PERSON] [4], [PERSON] [5], [PERSON] [6], [PERSON] [7], and other scholars have used the maximum likelihood method to study the modulation recognition technology. This method judges the modulation category of the signal by analyzing the statistical characteristics of the modulation signal. However, the computational complexity is high and the scope of application is narrow. Moreover, in the process of recognition, this method needs to master the prior information, such as signal frequency, baud rate, and signal-to-noise ratio, which is difficult to obtain accurately in the field of non-cooperative communication.  
The pattern recognition method based on feature extraction is to extract the characteristic parameters different from other modulation signals from the received signals, and then use the classifier to classify them to determine the modulation type of the signals. Common features include: wavelet domain features [8; 9], cyclic spectrum [10; 11; 12], and higher order cumulant [13; 14; 15; 16; 17; 18].  
[PERSON] [13] proposed a method combining high-order cumulant with support vector machine to successfully identify 2 ASK, 4 ASK, QPSK, 2 FSK, and 4 FSK signals. [PERSON] [15] extracted the combination of second-order and eighth-order cumulants as characteristic parameters, and combined with support vector machine to realize the modulation recognition of mask, MPSK, MFSK, and MQAM signals. When the signal-to-noise ratio is 10 dB, theaccuracy can reach 100%. [PERSON] [16] took the combination of high-order cumulants as characteristic parameters and combined this with the random forest classification method to realize the inter class and intra class recognition of MPSK, MQAM, and MAPSK signals. [PERSON] [17] used the method of higher-order cumulant and logarithm to classify the modulation of MQAM. [PERSON] [18] combined high-order cumulant with a deep-learning method to realize four kinds of classification first, and then processed by high-order cumulant for in class recognition. [PERSON] [19] used high-order cumulants to identify BPSK, 8 PSK, and 16 QAM signals under low SNR in non-cooperative communication.  
The above studies mostly identify the signals in the standard database, but do not consider that the signals in the actual environment may be affected by the acquisition environment, acquisition mode, etc. Ref. [20] first collects signals in space through SDR equipment, and then uses an artificial intelligence classifier to train and recognize the collected signals. The amount of computation of the artificial intelligence classifier is large, and the real-time performance is not as good as the decision tree classifier.  
Therefore, according to the demand for real-time performance in engineering applications, combining software radio with modulation identification technology, an automatic modulation identification, and a detection system of electromagnetic interference signal based on SDR is proposed to realize the automatic detection and identification of six modulation types of electromagnetic interference signal modulation modes in space: BPSK, QPSK, 8 PSK, 8 QAM, 16 QAM, and 64 QAM.