Abstract:With the increasing complexity of the electromagnetic signal environment and the increasing number of communication devices, the interference with electromagnetic signals is gradually increasing. Therefore, the study on signal reception and processing techniques in different noise environments and the use of signal data indicators and the information they carry in complex electromagnetic environments is very critical. In order to understand the performance of noisy signals in different electromagnetic environments and improve the quality and reliability of signal utilization, a time series decomposition-based electromagnetic data processing method is proposed. A noisy signal processing model is established based on additive seasonal time series decomposition, and the model is also employed to analyze and evaluate the performance of signals in noisy environments with regularity, trend, BER, etc., and to data-mine the original information and carrier information. Compared with the traditional methods, the proposed time series decomposition-based signal mining and prediction model is more accurate for signal prediction in noisy environment.