# Bistatic Sea Clutter Suppression Method Based on Compressed Sensing Optimization
## 1 Introduction  
When detecting targets on the sea surface with radar, the existence of sea clutter presents great challenges to the detection task. Sea clutter refers to the backscattered echoes received by the radar from the sea surface, which comprises wind waves, gravity waves, and surface capillary waves, etc. In the sea target detection of bistatic radar, since the power of the sea clutter is much higher than the target echo, the target echo is usually submerged in the sea clutter, making it difficult for bistatic radar to accurately detect sea surface targets. Moreover, the existence of sea clutter will lead to false alarm or missing alarm in detection of sea surface targets. Therefore, it is very crucial to suppress sea clutter to achieve accurate detection of sea surface targets.  
Currently, the traditional methods for sea clutter suppression can be broadly classified into three categories. The first category involves establishing models to fit the distribution of the sea clutter by studying the change characteristics of the sea clutter [1]. The second category is based on processing in the transform domain, including short-time Fourier transform (STFT), fractional Fourier transform (FRFT), wavelet transform (WT), etc. [2; 3; 4; 5]. The third category is based on subspace decomposition theory, such as singular value decomposition (SVD) [6; 7] and eigenvalue decomposition (EVD) [8]. However, the above methods have several disadvantages, including high computational requirements and limited application scenarios. Among these methods, the time-domain cyclic cancellation method (hereinafter referred to as root method) proposed by [PERSON] [9] is widely used in engineering due to its simple principle and low computational requirements. However, the root method's parameter estimation error can result in an unstable clutter suppression effect.  
On the other hand, the sparse representation theory inspires a new research idea for sea clutter suppression. For instance, [PERSON] et al. proposed a sea clutter suppression method based on sparse optimization to separate the target echo from the sea clutter echo [10]. Similarly, [PERSON] suggested a sea target extraction method based on sparse separation of sea clutter spectrum [11]. [PERSON] et al. explored three different formulas for sparse signal separation, utilizing short-time Fourier transform as a dictionary to separate stationary and moving targets from sea clutter with greater accuracy [12].  
Against this background, this paper makes distinct contributions as follows: We consider the main factors affecting bistatic sea clutter for the scenario of bistatic sea surface target detection and analyze and model the bistatic sea clutter. The analysis results show that bistatic sea clutter can be reconstructed and suppressed by using the idea of compressed sensing reconstruction. Therefore, the main idea of the proposed method is based on compressed sensing theory. Specifically, we propose a sparse base optimization algorithm based on transfer learning, which converts fixed sparse base into adaptive sparse base. Furthermore, we propose a sensing matrix optimization algorithm based on eigenvalue decomposition and gradient descent, which can improve the signal reconstruction quality by reducing the cross-correlation coefficient between the sparse base matrix. Finally, simulation results of sea clutter suppression demonstrate the effectiveness of the proposed method.