Research on Sea Clutter Simulation Method Based on Deep Cognition of Characteristic Parameters

Abstract

The development of radar systems requires extensive testing. However, field experiments are costly and time-consuming. Sea clutter simulation is of great significance for evaluating radar system detection performance. Traditional clutter simulation methods are unable to achieve clutter simulation based on the description of environmental parameters, which leads to a certain gap from practical applications. Therefore, this paper proposes a sea clutter simulation method based on the deep cognition of characteristic parameters. Firstly, the proposed method innovatively constructs a shared multi-task neural network, which compensates for the lack of integrated prediction of multi-dimensional characteristic parameters of sea clutter. Furthermore, based on the predicted clutter characteristic parameters combined with the spatial–temporal correlated K-distribution clutter simulation method, and considering the modulation of radar antenna patterns, the whole process of end-to-end simulation from measurement condition parameters to clutter data is accomplished for the first time. Finally, four metrics are cited for a comprehensive evaluation of the simulated clutter data. Based on the experimental results using measured data, the data simulated by this method have a correlation of over 93% in statistical characteristics with the measured data. The results demonstrate that this method can achieve the accurate simulation of sea clutter data based on measured condition parameters.

Keywords

• sea clutter
• characteristic parameters
• shared multi-task neural network
• integrated prediction model
• K-distribution
• end-to-end simulation method