*4.3. Selection of Wave Gate Number in Weighted Features Fusion*

Although SL0 and OMP reconstruction algorithms can accurately reconstruct the sparse echo data, the correct classification probability of the extracted features still lags behind that of the complete echo data. However, as we analyzed in Section 3.2, after using the weighted features fusion method for the features extracted from the multi-wave gates reconstructed echo data, the fused features are more clustered near the mean of all samples. Therefore, a classification algorithm based on the weighted features fusion of multi-wave gates reconstructed echo data is proposed in this paper in order to improve the classification probability. In this experiment, we compare the influence of the wave gate number on the probability of target classification in order to get the best wave gate number in weighted features fusion. The training and testing dataset of features are all extracted from the reconstructed multi-wave gates echo data. The experimental results are shown in Figure 12.

**Figure 12.** Selection experiment of wave gate number.

We can summarize from the above figure that the classification probability among the wave gate number from one to six in weighted features fusion increases with the raise of SNR. When only one wave gate reconstructed echo datum is used to extract features which is selected for classification, the probability is lower than that of multi-wave gates. When the SNR is low, the probability of choosing two fused wave gates features to classify aircraft targets is lower than that of choosing three to six wave gates fused features, but with increasing of the SNR, the probability of choosing two fused wave gates features is similar to that of using more. The experimental results also show that the classification probability curves of choosing three to six wave gates for weighted features fusion has the same change rule and is similar with each other under the same SNR. On the one hand, the experimental result shows that the classification effect of using multi-wave gates reconstructed echo data to classify three types of aircraft targets is better than that of using only one gate reconstructed echo datum. On the other hand, by fusing the features extracted from multi-wave gates reconstructed echo data, the fused features can be close to the mean value, and the number of cross-values of features extracted from different aircraft targets is reduced. However, if too many wave gates in weighted features fusion are selected, during this period, there are some differences between the extracted features due to the change of the aircraft's motion direction and flight attitude, and the probability of target classification does not increase with the increase of the number of wave gates. In summary, when the wave gate number in weighted features fusion is four, the classification probability of aircraft targets is the best.
