WANG Yaping, WEI Guanghui, PAN Xiaodong, et al. Research on Prediction Method of Out-of-band Dual Frequency Interference in Communication Equipment[J]. 2018, 44(7): 2268-2275.
WANG Yaping, WEI Guanghui, PAN Xiaodong, et al. Research on Prediction Method of Out-of-band Dual Frequency Interference in Communication Equipment[J]. 2018, 44(7): 2268-2275.DOI:
The existing sensitivity test method for communication equipment is usually used in single frequency interference condition
therefore the study on multi-frequency interference sensitivity test method is necessary since the space electromagnetic environment is increasingly complex. Based on the restart effect of the communication equipment due to being irradiated by the out-band strong field
we analyzed the response discipline of the communication equipment irradiated by dual-frequency interference. Moreover
the signal envelope was deduced from the form of the interfering signal
and a dual-frequency interference prediction model was established by leading the coefficients to representing the duty cycle. The model can determine whether the test system will restart or not by calculating the prediction coefficient in the case of known single-frequency sensitivity of the equipment under test. Through strong field irradiation test for communication station in GTEM room
it is determined that the duty ratio coefficient is 0.4
which is associated with the characteristics of the system under test
thus the out-of-band dual-frequency interference prediction model of the tested system is modified. At last
the correctness of the model is tested by dual frequency interference test. The experimental results show that the prediction model related to the effective value of the interference signals cannot correctly predict the degree of the tested system because some of the predict values may be less than 1
and the maximum forecast error of the prediction model related to the peak value of the interference signals will exceed 50%
therefor the prediction model including the duty ratio coefficient has better applicability than other two models because the forecast error is less than 12%.