Studies On Recognition Method Of Radar And Communication Signals Based On Spectral Correlation

With the continuous development of modern science and technology, modern warfare is transforming from the previous single-style confrontation to the system confrontation. This change makes countries all over the world strive to carry out the research on the integrative warfare technology for radar and communication. The domestic studies of single-system communication surveillance and radar reconnaissance have got great achievements, but the studies of the integration of the two are just getting started. As it is the background, this paper studies the automatic recognition of modulation types in the integrative reconnaissance technology for radar and communication signals.The automatic recognition of modulation types is a very important link in the study of the integrative reconnaissance technology for radar and communication signals. Only by correctly identifying the signal modulation, the extraction of parameters for the interception of signals is more targeted in the following process. As the complexity and unpredictability of wireless communications environment are increasing, especially in the modern electronic warfare environment, every country is widely using the low probability of intercept (LPI) techniques to achieve high ability to resist interception and interference. As the traditional recognition of modulation types is very sensitive to noise, it's hard to recognize modulation types and extract the parameters of signals in the case of low SNR. The signal processing methods basing on the theory of cyclic spectrum are widely used in the theoretical studies of signal analysis and parameter extraction for the benefits of insensitivity to the stationary interference and noise. For these reasons, the theory of cyclic spectrum is very suitable for the recognition of modulation types and extraction parameters of signals in the integrative radar and communication warfare. This study of modulation recognition in the case of low SNR is not only of important theoretical significance, but has very important application value. This article first introduces cyclic spectrum theory based on cyclostationary model, and then through calculating duty cycle of the received signal which is one of the given routine communication signals and pulse compression radar signals will be roughly seperated as one categorie in two (communication signals and the radar signals). After that, different methods are used to recognize the modulation types of communication signals and pulse compression radar signals. With the superior characteristics of second-order cyclic spectrum this paper uses time-variant finite-average cyclic autocorrelation algorithm (CA algorithm) to analize the spectral correlation characteristics of both communication signals (AM, FM (mf=0.3 and mf=20), ASK, FSK, BPSK and QPSK signal) and pulse compression radar signals(linear frequency modulation and two-phase Barker signal). At last by extracting the characteristic parameters the automatic recognition of modulation type is achieved.Simulation results show that using envelope detection to get duty cycle is effective to separate routine communication signals and the pulse compression radar signals with a appropriate duty cycle threshold. And in the case of low SNR, it is effective to identify the different modulation types of given routine communication signals and the pulse compression radar signals using CA algorithm. The appropriate thresholds are also needed. Finally the correctness and feasibility of the algorithm is verified by simulation.