# Adaptive Clutter Intelligent Suppression Method Based on Deep Reinforcement Learning
## 5 Experiment and Result Analysis
### Analysis of Suppression Effects under Measured Clutter Data
From Figure 4, it can be seen that when the target is located at the 23 rd range cell and is in clutter region 1, the clutter center frequency is 0 Hz. At this time, the three-pulse MTI can suppress the clutter in clutter region 1. Since the target's frequency is located at 440 Hz, which is farther away from the zero frequency, the target is not completely eliminated, but there is a loss of the target signal power, which is 37.5464 dB, and the target signal is reduced by 7.4536 dB. Due to the higher frequency of the clutter in clutter region 2, it can be seen that the clutter in this region is not completely suppressed in Figure 4a, but the clutter is attenuated after the three-pulse canceller. Figure 4b,c show the comparison of the clutter suppression of the traditional eigenvector method and the proposed method. Clutter 1 and 2 regions are suppressed because the two methods produce a depression in the center of the clutter frequency, so as to carry out the suppression of the clutter. The maximum power of the target after the processing of the two methods is 40.0157 dB and 41.0291 dB, respectively, the given target power is 45 dB, and the comparison of the data shows that the loss of target power is minimized after the clutter processing of the proposed method in this paper. From Figure 5, it can be seen that for the clutter with a certain center frequency, the MTI method cannot effectively suppress the high-frequency clutter, and the target in clutter region 2 is still masked after the MTI three-pulse canceller. While the traditional eigenvector method and the proposed method are still effective in suppressing the clutter to highlight the target, the target signal power is reduced compared to the experimental results of the clutter at 0 center frequency. This is due to the clutter center frequency of clutter region 2 being at 150 Hz. When the feature vector method predicts the clutter center frequency and produces a depression at 150 Hz, while the target is located at 440 Hz, the target frequency is closer to the clutter center frequency, which inevitably attenuates the signal of the neighboring frequency, resulting in a reduction in the target signal power. The maximum power of the target is 35.5389 dB and 36.5576 dB, and the SCNRs are 21.2227 dB and 22.9845 dB, respectively. The filter designed by the proposed method in this paper still outperforms the traditional eigenvector method in clutter region 2. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Clutter Region 1** & **Clutter Region 2** & **Clutter Region 3** \\ \hline
**Average Clutter Power in Clutter Region** & **90.3762** & **80.7891** & **70.9746** \\ \hline Range cell of target & 138 & 635 & 1100 & 1520 & 1520 & 3860 \\ MTI method & 7.5789 & 7.1721 & 7.0347 & 7.3106 & 6.9516 & 7.1702 \\ SVD method & 10.2088 & 8.0744 & 5.9770 & 4.2950 & Invalid & Invalid \\ Traditional eigenvector method & 8.3439 & 7.2806 & 9.1280 & 9.2615 & 9.0879 & 9.8341 \\ Proposed method & 16.0097 & 15.4120 & 15.8478 & 15.6033 & 14.3195 & 12.6496 \\ Proposed method output SCNR gain & 26.0097 & 25.4120 & 25.8478 & 25.6033 & 24.3195 & 22.6496 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Clutter suppression results of each method in different clutter intensity environments. Figure 6a shows the filter response curves of the three methods when the target is located in the 23 rd range cell. The MTI three-pulse canceller generates a notch at the zero frequency, and the depth of the notch meets the need for clutter suppression, but the width of its zero-frequency notch is insufficient to completely filter out the clutter. The traditional eigenvector method produces a notch at the center of the corresponding clutter frequency to suppress the clutter under the prediction of clutter information, which can completely filter out the clutter but inevitably loses part of the signal energy. The method proposed in this paper explores and exploits the same predicted clutter spectrum near the center, optimizes the parameter settings of the filter, and changes the filter width.