A proposed model for processing UAV signals in the presence of WiFi co-channel interference based on compressive sensing and machine learning
192 viewsDOI:
https://doi.org/10.54939/1859-1043.j.mst.82.2022.70-80Keywords:
Compressive sensing; WiFi interference; Machine learning.Abstract
This paper proposes a model for detecting WiFi signals in the presence of co-channel Flycam signals. The proposed method uses compressed sensing and machine learning techniques in the case that Flycam and WiFi signals are co-channel. A particular compressed sensing matrix is designed to sample the first period of the signal segment, including 256 samples. The matrix output is a vector 1x64 which is used as 64 features of the WiFi signal for the AI model based on the LOF algorithm to classify WiFi signals and non-WiFi signals. The number of samples required to remove WiFi from the mixed signal is reduced by extracting the features of the WiFi signal at the beginning of the signal segment rather than the entire signal sample. The WiFi signal classification method using the proposed model has an accuracy of 93.25% with SNR = 30 dB and above 70% with SNR = 15 dB and a faster execution time than formula-based and calculation-based classification with the total number of samples.
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