Hybrid Model–Data-Driven Radar Jamming Effectiveness Evaluation Method for Accuracy Improvement - Runyang Chen; Yi Zhang; Xiuhe Li; Jinhe Ran; Qianqian Shi (Remote Sensing)
Accurate and effective radar jamming effectiveness evaluation is the key to forming the radar jamming OODA (Observe, Orient, Decide, Act) loop. The current model-driven evaluation method has low confidence and the data-driven evaluation method lacks sufficient high-quality data; thus, the evaluations that are solely model-driven or data-driven are not effective. In order to solve this problem, we propose a radar jamming effectiveness hybrid model–data-driven evaluation method. Firstly, the mechanism of the model is constructed based on the jamming equations. Secondly, the quality of training data is improved by data cleaning and model correction, after which the hybrid model is realized by training with simulated data and fine-tuning it with real-world data. Finally, the validity of the method is proved by simulation experiments, which show that the method is capable of effectively improving the accuracy of prediction and evaluation, and has good practicability. Compared with the model-driven method, the RMSE (Root Mean Square Error) of the prediction results of this method is reduced by 88.26% and the MRE (Mean Relative Error) is reduced by 92.00%.