To address the problem of low 3D positioning accuracy caused by multipath interference and non-line-of-sight propagation, this paper proposes an adaptive Kalman filter-based Chan-Taylor fusion positioning algorithm. First, an improved KF is applied to preprocess the TDOA-based measurements to suppress noise interference. Then, the Chan algorithm and the Taylor algorithm are respectively used to compute the position estimates based on the preprocessed data, providing initial location estimates. Finally, the difference between the initial estimates obtained by the Chan and Taylor algorithms is used as the iteration trigger condition: If the difference exceeds a predefined threshold, the algorithm enters Taylor iteration; otherwise, the initial position estimate is directly output. Simulation results show that, compared with the Chan algorithm, the KF-Chan algorithm, the Chan-Taylor algorithm, and the weighted Chan-Taylor algorithm, the proposed method improves positioning accuracy by 87.64%, 75.95%, 53.52% and 40.30%, respectively.