Performance Evaluation System Based on Multi-Indicators for Signal Recognition

Nowadays, the electromagnetic environment is becoming more and more complex, and it is increasingly difficult to accurately identify electromagnetic signals. At the same time, the quality of models and algorithms largely determines the final result of electromagnetic signal recognition, and a comprehensive and holistic evaluation system is needed to assess the quality of model or algorithm performance in the field of electromagnetic signal recognition. To address this phenomenon, this paper uses different machine learning models and AdaBoost algorithms to identify 100 classes of WiFi radiation source electromagnetic signals. In this paper, the classification performance, complexity and robustness of the models are evaluated comprehensively in three aspects respectively, and a multi-dimensional evaluation system for signal recognition is constructed, and the differences between different model algorithms in signal recognition effects are compared and analyzed, and the correlations between each evaluation index in different dimensions are explored. The experimental results show that different machine learning models and algorithms have different recognition effects on electromagnetic signals, among which the recognition accuracy of ResNet can reach more than 90%, but its computational complexity is high and easily affected by noise. DNN has poor recognition effect and the highest computational complexity, but it is not easily affected by noise. The AdaBoost algorithm does not necessarily improve the recognition classification accuracy of the underlying classifier. The evaluation system established in this paper is meaningful for assessing the performance of models and algorithms.