# Adaptive Clutter Intelligent Suppression Method Based on Deep Reinforcement Learning
## 5 Experiment and Result Analysis
### Analysis of Suppression Effects under Measured Clutter Data
Meanwhile, in the clutter-noise mixing region, where both clutter and noise exist, the spectral analysis of this range cell is more complex, which makes the agent require more frequent and complex adjustments for exploration and exploitation, thus affecting the optimal parameter configuration of the filter. This leads to the difficulty of achieving the same optimization effect for agents in the hybrid noise and clutter region in the same training rounds. In the noise region, the energy is more dispersed, the spectrum of the clutter signal is not concentrated, the energy is low, and the spectral characteristics are not as obvious as in the strong sea clutter region and the clutter-noise mixing region. Moreover, the average clutter power in the noise region is 70.9746 dB, the energy of the clutter signal is lower than that of the target signal, and the spectra of the clutter and the target signal overlap more, so it is difficult to find obvious spectral characteristics to locate the clutter region. Figure 8: Radar echo image before clutter suppression. (**a**) Two-dimensional echo signal view; (**b**) range dimension image of the first pulse. the center of the clutter, which leads to the clutter suppression effect not being as effective as in the strong sea clutter region and the clutter-noise mixing region. Table 8 shows the statistics of SCNR before and after the clutter suppression of each method in different clutter strength environments. It can be seen that the MTI method is effective in different clutter environments, and the output SCNR is stabilized at about 7 dB. The effect of environments with different clutter strengths on the performance of the MTI method is not significant, which is due to the fact that the notch depth of the MTI filter cannot completely suppress the clutter component of this range cell, and there is a certain Doppler shift in the sea clutter, so the suppression effect is limited. The suppression performance of the SVD method shows a decrease in the output SCNR or even an invalid suppression of the clutter as the clutter environment changes. This is because the clutter signal is easily recognized and removed when the power of the clutter signal is much larger  
Figure 9: Three-dimensional view of the proposed method after clutter suppression. (**a**) The simulation target is located at the 138 th range cell. (**b**) The simulation target is located at the 635 th range cell. (**c**) The simulation target is located at the 1100 th range cell. (**d**) The simulation target is located at the 1520 th range cell. (**e**) The simulation target is located at the 2500 th range cell. (**f**) The simulation target is located at the 3860 th range cell. than the power of the target signal, so the singular value decomposition (SVD) method effectively separates the clutter signal and suppresses it in clutter region 1. In clutter region 2, the average power of the clutter is about 80 dB, and the energy of the target signal is close to that of the clutter signal, so it is difficult for the SVD method to clearly distinguish between the target signal and the clutter signal during the decomposition, which leads to a decrease in the effect of separating the signal from the clutter. Clutter region 3 is a noise region without obvious sea spikes, and the target signal energy is higher than the clutter energy, the target is mistakenly canceled as sea clutter, and the SVD method is invalid. Compared with the MTI method, the traditional eigenvector method increases the SCNR by about 2 dB in each clutter region, while the proposed method further increases the SCNR by adding a deep Q-network to interact with the environment and continuously learning, which makes the position of the filter notch \"more accurate\", \"deeper\", and \"wider\" and further improves the output SCNR to about 15 dB in average. Overall, the proposed method exhibits excellent SCNR in different clutter environments.