To address the limitations of traditional Fresnel zone-based indoor localization methods in terms of hardware offset sensitivity and multipath interference. This paper proposes an indoor localization model based on Fresnel clustering fusion, an indoor localization model based on Fresnel clustering fusion is proposed, which consists of two key stages: Firstly, A phase calibration model based on Differential Semidefinite Programming (SDP) has been developed to effectively avoid local optima during nonlinear offset correction and achieve globally optimal results. Additionally, a residual attention autoencoder was integrated to enhance the reflection characteristics of high-frequency targets and reduce multipath interference. Secondly, a localization method that integrates Fresnel zone analysis based on clustering fusion is proposed, which optimizes the feature space and addresses the issue of boundary ambiguity through a KL divergence constraint to achieve accurate target localization. Furthermore, the clustering centers are mapped to the intersection points of the Fresnel ellipse for precise localization targets. Experimental results demonstrate that the proposed method achieves a localization accuracy of up to 0.7 m.