A Radar TR Component Electromagnetic Interference Signal Strength Prediction Model Based on IGWO-SVR

As a key sensor, radar plays an important role in obtaining war information. However, radar will be affected by the deteriorating electromagnetic environment on the battlefield. Therefore, it is necessary to carry out electromagnetic interference effect experiments to improve the anti-interference ability. In the electromagnetic interference experiment of the radar TR component, the signal output from the signal generator passes through the power amplifier, the transmitting antenna and the receiving antenna, and then is applied to the TR component. In the experiment, to obtain a certain size of interference signal acting on the TR component, it is often necessary to manually adjust the output signal strength of the signal generator repeatedly through human experience. The experimental process is complicated, and the experimental error is large. Therefore, it will make the experiment more convenient and accurate to achieve the desired interference signal size on the TR component by accurately predicting the output signal strength value of the signal generator, which has important practical significance. This paper proposes an IGWO-SVR based signal generator output signal strength prediction model, which includes Improved Grey Wolf Optimizer (IGWO) and Support Vector Regression (SVR) algorithms. IGWO is a new swarm optimization algorithm proposed in this paper. By improving the convergence factor a and the final position of
$\omega $
wolf, IGWO solves the problems that the traditional GWO algorithm easily falls into local optimum and the convergence speed is slow. IGWO is used to optimize two hyperparameters of SVR (penalty coefficient C and kernel parameter
$\gamma $
). SVR is used to predict the output signal strength value of the signal generator. To prove the validity of the IGWO-SVR, comparison experiments are made between the IGWO-SVR and 20 other models. The real data obtained from the experiments of electromagnetic interference effect by irradiation method are selected as the experimental data. Mean Absolute Error (MAE), Mean Squared Error (MSE), and Fitting Degree R Squared
$(R^{2})$
are used to evaluate the overall performance of the models. Through comparative experiments, the MAE of the IGWO-SVR model is 1.1481, MSE is 2.6679,
$R^{2}$
is 0.9430, and its performance in various evaluation indexes is better than other models.