A Model for Detecting Targets and Classifying Pulses of 6-Antenna Radar Systems with Neural Networks Optimized by Genetic Algorithm

In this research, a model for detecting targets and classifying the pulses received by the 6-antenna radar system using artificial neural networks optimized by genetic algorithm is presented. The proposed model consists of two main parts: clustering and classification. In the clustering process, the different pulses received by each of the radar antennas are clustered in such a way that the pulses of each target are placed in the cluster of the same target, and finally the results of clustering with different algorithms are evaluated by Dunn index. In the classification process, using the neural network, the target angle is predicted, the characteristics of which are received by the antennas, and the weights and biases of the neural network are optimized by a genetic algorithm. To adjust the parameters, Taguchi method has been used to select the best values of the parameters and the neural network has been trained with these values, and as a result, the accuracy of predicting the received pulse angle has increased to 98.55%.