Enhancing jamming source tracking capability via adaptive grey wolf optimization mechanism for passive radar network

In a complex electromagnetic environment, the tracking of jamming source by passive radar network is of great significance for enhancing anti-jamming capability, military combat safety, and strategic decision-making. However, traditional jamming source tracking algorithms suffer from low tracking accuracy and convergence speed, primarily due to the high nonlinearity and the unknown noise characteristics of the passive radar system. In order to improve the capability of jamming source tracking for passive radar network, a maximum correntropy cubature Kalman filter based on improved grey wolf optimization algorithm is proposed. Firstly, the grey wolf optimization mechanism improved by Gaussian random walk and Gaussian mutation strategies is proposed to accurately estimate the characteristics of unknown process and measurement noise, providing more accurate model parameters for the cubature Kalman filter algorithm. Then, an adaptive maximum correntropy criterion is designed, which optimizes the filter gain by adaptively adjusting the kernel size to suppress the influence of outliers on the filtering estimation and enhances the robustness of the algorithm. Finally, experiment of jamming source tracking indicates that the proposed algorithm significantly outperforms traditional algorithms in terms of tracking accuracy and convergence speed under diverse unknown noise environments.