Adaptive interference suppression method based on log-Euclidean function

Passive detection of targets of interest (TOI) in strong interference environments poses a challenge. This paper proposes an adaptive interference suppression method based on the Log-Euclidean (LE) function, suppressing interference to enhance the direction of arrival (DOA) resolution. TOI-bearing intervals were estimated using conventional beamforming. The sample covariance matrix (SCM), processed with Toeplitz theory, is decomposed using eigen-decomposition. The correlation between each eigenvector and the steering vectors is then calculated to identify those corresponding to TOI. Interference-related eigenvectors are removed from the SCM to reconstruct a refined SCM. Finally, the Log-Euclidean (LE) function is applied for spatial spectrum estimation, reducing interference power and sidelobes near the TOI main lobe. This provides theoretical support for subsequent DOA estimation. Simulation and experimental results show that the proposed method effectively suppresses ship and other interferences, reducing maximum interference power by over 20 dB and sidelobes near the main lobe by more than 20 dB. This improves DOA resolution. Compared to existing methods, it offers more reliable and robust interference suppression.