Due to the complexity of underwater conditions, achieving stable long-endurance autonomous underwater navigation has always been a challenging issue. Polarized light navigation, which utilizes the polarization field in the underwater downward radiation field to determine the heading angle, requires a known horizontal attitude beforehand. In response to the significant deviations caused by interference in the existing underwater polarization attitude determination algorithms, this paper proposes an edge recognition method that integrates the Power theorem of circles and Improved 3D Conical Hough Transformation (PTC–3D-CoHT). This method has the advantages of pre-screening effective pixel points, better handling of distorted circles, and improving the deviation in extracting Snell’s window. The theoretical basis, model, and detailed calculation process of this method are provided in this paper. Underwater experiments show that, compared to the Circular Hough Transformation (CiHT) and 3D Conical Hough Transformation (3D-CoHT) algorithms, PTC–3D-CoHT enhances the robustness of Snell’s window extraction, verifying the effectiveness of the proposed method.