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The measurement noise of the standard Kalman filter (SKF) and related filtering algorithms are usually required to obey Gaussian distribution. However, it is hard to be satisfied in practice and the reliability of the filtering results is reduced when the measurement noise is not normally distributed. The robust Kalman filter (RKF) has the ability to bound the abnormal observations by weighing the influence of the updated parameters in accordance with the magnitude of discrepancy between the updated parameters and the robust estimates obtained from measurements at each time step. However, there is a limitation that it wastes the useful information of the state model sometimes. In order to solve the problem of magnetic interference compensation under noise uncertainty, this paper addresses a new method based on the robust M estimation to make the uncertain noise suppressed. The double-model adaptive estimation (DMAE) approach is proposed, which consists of a SKF and a RKF. Two filters are selected adaptively by a conditional probability according to the innovation covariance at each time step. More specifically, the DMAE has ability to resist the measurement noise and make full use of effective information at the same time. Compared with the SKF and the RKF method, the DMAE achieves a better performance in terms of compensation accuracy and robustness for parameter estimation. After compensation, errors of components are reduced from 502, 613, 493 to 3.9, 2.1, and 23.9 nT, respectively, with the proposed method, the ideal accuracy of total field is achieved as well. [ABSTRACT FROM AUTHOR]