# Adaptive Clutter Intelligent Suppression Method Based on Deep Reinforcement Learning
## 5 Experiment and Result Analysis
### Analysis of Suppression Effects under Measured Clutter Data
It can be seen that the filter notch designed by the method proposed in this paper is much narrower and is able to completely suppress the clutter while having a higher amplitude-frequency response to signals at non-clutter frequencies, thus reducing the loss of the target signal. In Figure 6b, the conventional eigenvector method produces a notch near 150 Hz, on which the proposed method further optimizes the filter notch width and position to suppress clutter while reducing the target signal loss, while the three-pulse cancellation still produces a notch at the zero frequency, which is unable to suppress the non-zero-frequency clutter. In conclusion, by analyzing the above four sets of experiments, it can be found that the proposed method in this paper outperforms the MTI three-pulse canceller and the traditional eigenvector method in terms of the combined performance of clutter suppression processing and the degree of target power loss. From Figure 7a, it can be seen that when the clutter is located at the zero frequency, the output SCNR of the three methods is significantly improved. As the target speed decreases, the output SCNR gradually decreases, due to the fact that the low-speed target is close to the zero frequency, which overlaps with the clutter frequency resulting in poorer clutter suppression. When the clutter is located at 150 Hz, it can be seen from Figure 7b that the notch generated by the MTI three-pulse canceller at zero frequency cannot completely suppress the clutter at 150 Hz, but the clutter is attenuated. In contrast, the traditional eigenvector method and the proposed method in this paper can move the notch of the filter to 150 Hz and suppress the clutter completely. As the speed decreases and the Doppler frequency of the target is close to 150 Hz, the clutter suppression effect of the two methods deteriorates, but the adaptive clutter intelligent suppression method based on DRL can still suppress the clutter while minimizing the loss of the target signal, and its output signal heterodyne noise is still higher than that of the traditional eigenvector method. When there are two clutter center frequencies of clutter at the same time, the suppression effects of the three methods are shown in Figure 7c, the MTI three-pulse method does not work for the non-zero-frequency clutter, and it still plays the role of clutter attenuation. Meanwhile, the traditional eigenvector method and the method proposed in this paper can generate double notches in the frequency domain according to the spectral centers of different clutter and generate notches at \(-150\) Hz and 150 Hz at the same time, both of which can completely suppress the clutter, but the filter designed by the method proposed in this paper has a higher output SCNR and a lower loss of target power compared to the filter designed by the traditional eigenvector method. In conclusion, the adaptive clutter intelligent suppression method based on DRL shows higher output SCNR and lower target power loss under different target speeds and different clutter frequencies, which proves its superior performance and strong adaptive ability in complex environments. In addition, through the dynamic adjustment and optimization of the filter parameters, the method can effectively respond to environmental changes, avoid the performance degradation caused by the complexity and uncertainty of the environment, and improve the stability of the system.