*4.4. Classification Experiment Based on Weighted Features Fusion with Four Wave Gates Sparse Echo Data*

In this section, based on the target classification algorithm of multi-wave gates in weighted features fusion proposed in this paper, we conduct two comparative simulation experiments. One is that we train the SVM model with the dataset composed of single wave gate complete echo data, classify aircraft targets with the dataset consisting of four wave gates complete echo data and the dataset consisting of four wave gates reconstructed echo data respectively. The simulation results are shown in Figure 13. Another comparative simulation experiment is that we train the SVM model with the dataset composed of four wave gates complete echo data, while the testing datasets consist of four wave gates complete echo data and of four wave gates reconstructed echo data, respectively. The experimental results are shown in Figure 14.

**Figure 13.** Classification results of single wave gate echo data for training and four wave gates echo data for testing.

**Figure 14.** Classification results using four wave gates echo data for both training and testing.

Compared with the experimental result of single wave gate echo data as the testing dataset in Figure 11, we can conclude from the results of four wave gates echo data that the best way to classify targets, as testing dataset in Figure 13 that the classification probability of the complete echo data is obviously improved. The classification probability of features extracted from reconstructed echo data by SL0 and OMP algorithms after weighted features fusion is also higher than that of features without weighted features fusion. Therefore, we come to the conclusion that the echo data of multi-wave gates to classify the aircraft targets can improve the correct classification probability.

Comparing the experimental results in Figures 13 and 14, the classification probability of using four wave gates complete echo data for both training and testing is better than that of the single wave gate complete echo data for training and four wave gates complete echo data for testing, and the reason for this is that the SVM model can learn more target echo information by using four wave gates echo data. Moreover, the classification probability of the two kinds of reconstruction algorithms can reach 99.83% in Figure 14, which verifies the validity of both kinds of reconstruction algorithm and the effectiveness of the classification algorithm based on weighted features fusion of multi-wave gates reconstructed echo data. Therefore, it can be summed up that in the process of radar classification of three types of aircraft target, we can use four wave gates echo data as far as possible in weighted features fusion for training the SVM model, in which the parameters of the SVM model trained in this way are optimal. Also the classification probability of the target is highest when testing with four wave gates echo data.
