# Intelligent Simulation Technology Based on RCS Imaging
## 4 Analysis of Experimental Results  
The performance of the composite object is evaluated by designing a 10 GHz frequency test system for experimental verification. Figure 13 shows the system. The computer uses the serial debugging assistant software to send the corresponding instruction statements for controlling the start of RF transceiver components and the setting of parameters. The horn antenna is used as the transceiver antenna of the test system. The vector network analyzer is used to set the transmitting continuous wave point frequency. Figure 14 shows the actual product processed according to the simulation model. Figure 15 shows the test results. It can be observed that when the simulated target drone is placed horizontally at the frequency of 10 GHz, the peak RCS values appear around \(-180^{\circ}\), \(-152^{\circ}\), \(-90^{\circ}\), \(-39^{\circ}\), \(-9^{\circ}\), \(0^{\circ}\), \(9^{\circ}\), \(39^{\circ}\), \(90^{\circ}\) and \(152^{\circ}\). The RCS values at \(-180^{\circ}\), \(-152^{\circ}\), \(-90^{\circ}\), \(-39^{\circ}\) and \(-9^{\circ}\) are 21.26 dBsm, 12.37 dBsm, 38.17 dBsm, 17.14 dBsm and 13.09 dBsm, respectively. The RCS values at \(0^{\circ}\), \(9^{\circ}\), \(39^{\circ}\), \(90^{\circ}\) and \(152^{\circ}\) are 25.49 dBsm, 12.66 dBsm, 17.14 dBsm, 38.97 dBsm and 12.37 dBsm, respectively. The RCS value at \(0^{\circ}\) is 1.39 dBsm compared with the corresponding actual value of an aircraft, and the RCS values at other peaks also decreases within 3 dBsm of the aircraft. The test results show that the assessment index of less than 3 dBsm test error is satisfied. Equation (18) shows that the fidelity score is 93.1 points, which indicates a very realistic level and high performance of the simulation, according to Table 1. When the vector network analyzer is measuring, there will be a fluctuation of \(\pm\)1 dB, which is a systematic error. Therefore, at the position of the  
Figure 12: Comparison results from \(-30^{\circ}\) to \(30^{\circ}\) of aircraft’s tail and tail combination. Figure 13: Test system for the processed physical object. symmetric angle, the RCS value will include a certain test error, but as long as it does not exceed 1 dB, the test result can be proven to be correct. Deep learning algorithms fall into the following four main categories: convolutional neural networks, recurrent neural networks, generative adversarial networks, and reinforcement learning networks. The convolutional neural network includes a convolutional layer, a pooling layer and a fully connected layer, which is used to learn the features in the image and perform classification and detection. Figure 16 shows the classification of deep learning algorithms. Figure 14: Target simulation of each part. Figure 15: Radar cross section distribution characteristics of the target simulation. Therefore, the simulated targets are added into a data set and classified by fuzzy clustering or deep learning algorithms. Figure 17 shows the schematic diagram of intelligent shifting for target recognition. The data are collected through the data acquisition module of the single chip microcomputer, and then transmitted to the computer by Wi-Fi. When the identification result is one of the classification results in 1-4, the computer transmits instructions to the relay of the single side machine, and the relay closes the corresponding gear and outputs the corresponding Doppler frequency characteristics. Examples of simulation targets include four types of aircraft, marked as 1, 2, 3 and 4, respectively. Figure 18 shows the classification results. Figure 16: Classification of deep learning algorithms. Figure 17: The schematic diagram of intelligent shifting for target recognition. Figure 18: Identification result of four aircrafts.