# Adaptive Clutter Intelligent Suppression Method Based on Deep Reinforcement Learning
## 5 Experiment and Result Analysis
### Analysis of Suppression Effects under Measured Clutter Data  
The performance of the sea clutter suppression method is analyzed using data from the sea-detecting X-band experimental radar [27, 28], located at the sea target detection test site in the coastal area of Yantai, Shandong Province. The experimental data are selected as pure sea clutter data from the scanning radar echo data numbered \"20210106155330_01_staring\", which takes 64 pulses and 4346 range cells. The parameters of the X-band experimental radar are shown in Table 7.  
The 2D echo signal and the first pulse echo signal of the radar data are shown in Figure 8. For the 2D echo signal shown in Figure 8, the sea clutter amplitude in this data decreases with the increase in distance, and it can be roughly divided into three clutter regions: Clutter region 1: the first 1000 range cells are the strong sea clutter region, and the average power of the sea clutter is 90.3762 dB. Clutter region 2: 1001-2000 range cells are the clutter-noise mixing region, and the average power is 80.7891 dB. Clutter region 3: 2001-4346 range cells are essentially the noise region, and the average power is 70.974 dB. In this section, the experiments will test the clutter suppression effect of the four methods under different clutter intensity regions of the real data and the simulation target with an input SCNR of \(-\)10 dB, respectively. The 138 th and 635 th distance cells are selected to be added to the simulation target in the clutter region 1 where the average power of sea clutter is about 90 dB, the 1100 th and 1520 th distance cells are selected to be added to the simulation target in the clutter region 2 where the average clutter power is about 80 dB of clutter-noise mixing, and the 2500 th and 3860 th range cells are selected to be added to the simulation target in the noise region of clutter region 3 with an average power of about 70 dB. A total of six sets of experiments are conducted, and Figure 9 shows the results of the proposed method after clutter suppression. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Sea data file & 20210106155330\_01\_staring \\ Carrier frequency (GHz) & 9.3–9.5 \\ Pulse repetition frequency (Hz) & 1697 \\ Polarization mode & HH \\ Bandwidth (MHz) & 25 \\ Pulse width (us) & 3 \\ Azimuth angle (\({}^{\circ}\)) & 42.18 \\ Sea state & 3–4 \\ Distance resolution (m) & 6 \\ Mode of operation & staring \\ \hline \hline \end{tabular}
\end{table} 
Table 7: X-band experimental radar parameters. Figure 7: Output SCNR comparison of three methods: (**a**) simulated clutter center at 0 Hz, (**b**) simulated clutter center at 150 Hz, and (**c**) simulated clutter centered at \(-\)150 Hz, 150 Hz. From Figure 9, it can be seen that the adaptive clutter suppression method based on DRL can still effectively suppress the sea clutter under different clutter intensity environments, but there are some differences in the clutter suppression effect of different range cells. In the strong sea clutter region and the clutter-noise mixing region, the clutter suppression effect is significantly better than in the noise region. This is because the sea clutter energy in clutter region 1 and clutter region 2 is higher than the target energy when the input SCNR is \(-10\) dB, and the sea clutter spectral characteristics are more obvious. After obtaining the estimated clutter spectral center and the spectral width, the agent explores and exploits near the clutter spectral center to optimize the parameter settings of the filters and to change the width of the filters. In the case of minimizing the loss of the target signal, a notch is formed in the center of the clutter, and the sea clutter in that range cell is completely filtered out. Meanwhile, due to the different distribution of sea clutter in each range cell, the exploration and utilization strategies of agents will be different. For example, at range cell 138, the proposed method is able to achieve a higher output SCNR gain with a lower input SCNR.