# Adaptive Clutter Intelligent Suppression Method Based on Deep Reinforcement Learning
###### Abstract  
In the complex clutter background, the clutter center frequency is not fixed, and the spectral width is wide, which leads to the performance degradation of the traditional adaptive clutter suppression method. Therefore, an adaptive clutter intelligent suppression method based on deep reinforcement learning (DRL) is proposed. Each range cell to be detected is regarded as an independent intelligence (agent) in the proposed method. The clutter environment is interactively learned using a deep learning (DL) process, and the filter parameter optimization is positively motivated by the reinforcement learning (RL) process to achieve the best clutter suppression effect. The suppression performance of the proposed method is tested on simulated and real data. The experimental results indicate that the filter notch designed by the proposed method is highly matched with the clutter compared with the existing adaptive clutter suppression methods. While suppressing the clutter, it has a higher amplitude-frequency response to signals at non-clutter frequencies, thus reducing the loss of the target signal and maximizing the output signal-to-clutter and noise rate (SCNR). clutter suppression; adaptive intelligent suppression; deep reinforcement learning (DRL); deep Q-network (DQN) 2024 202  
Fourier transform (FRFT). Through the optimal FRFT transform, the target signal and clutter signal in the echo signal can be separated in the fractional domain, and the SVD is used in the fractional domain to further improve the distinction between the target signal and clutter. However, this method can only be used when the signal-to-clutter ratio (SCR) is high and the Doppler modulation of the target echo signal is approximately linear frequency modulation (LFM). When the SCR is low, it is easy to extract the clutter signal and suppress the target signal. Article [14] proposed a method combining SVD and a projection algorithm, constructing the clutter space of the neighboring cells to be detected. In the projection space, SVD is used on the clutter space to achieve clutter suppression. However, this algorithm requires that the range cells to be detected have a strong correlation with the neighboring cells; otherwise, it is difficult to achieve the desired suppression effect. Time-domain cancellation methods include cyclic cancellation [15; 16], Moving Target Indications (MTI) [17; 18], Adaptive Moving Target Indications (AMTI) [19; 20], etc. The cyclic cancellation method first estimates the parameters of the clutter signal model, thereby reconstructing the sea clutter in the time domain and subtracting it from the echo signal to achieve clutter suppression. However, it is necessary to establish a model that matches the real clutter characteristics. Due to the fact that the existing clutter models are not universal, the effect of clutter suppression in practical applications is not significant, and the cyclic elimination method faces certain difficulties in practical engineering applications. The traditional MTI method mainly uses two-pulse and three-pulse cancellers to suppress stationary clutter. Its implementation method is to generate a depression at zero frequency to eliminate the stationary clutter with a zero-frequency center. This method is simple to implement, but it is not effective for clutter with a certain Doppler frequency, such as sea clutter and meteorological clutter, which have complex non-stationary, nonlinear, and non-Gaussian \"three non\" characteristics [21]. The design idea of the AMTI method is to find a set of filter coefficients that can effectively suppress clutter and ensure the target signal passes through without loss. The eigenvector method is the main optimization design method of the MTI filter.