Abstract: ‍ ‍Ship target detection in synthetic aperture radar （SAR） imagery has always been an important means in the field of maritime surveillance. Classical constant false alarm rate （CFAR） detection relies on distribution models and accurate estimation of multiple parameters， making it difficult to adapt to complex and variable sea surface backgrounds. Emerging information geometry ship detection methods， although exploiting the statistical differences between targets and clutter to achieve a salient representation of the ship， are still limited by the accurate modeling of background clutter. Considering the limitations of existing methods， this paper proposes an algorithm based on eigenvalue decomposition of the Toeplitz matrix for ship target detection in SAR imagery. Without seeking a model of the background clutter distribution， the difference between the target and the background clutter is adequately obtained by constructing a Toeplitz matrix with its eigenvalue mean as the test statistic. The experimental results on the measured SAR images from the Gaofen-3 and TerraSAR-X satellites demonstrate that our method achieves better detection performance and faster computational speed compared to a variety of typical methods.