RD Image Datasets of OTHR Interference

This paper proposes a novel idea for the detection of the radio frequency interference (RFI) and the transient interference (TSI) in sky-wave over-the-horizon (OTH) radar, based on the classification of the range-Doppler (RD) images, since the RFI, the TSI and no interference (NoI) have different textures in the RD map. The RD image classifiers and the image databases are designed for the first time. The performances of image classification and interference detection are evaluated based on the image database that is constructed based on real data of OTH radar. New models are developed for the RFI and the TSI to construct the simulated database, so that the classifiers can use it as the training set, in no need of other data for its designing. Four classification algorithms are employed for the classifier design, where three textural features can be extracted. Experimental results show that the RD image classifiers obtain over $95\%$ classification accuracy and detection probability, at a cost of false alarm probability less than $2\%$.