In this paper, we investigate the method of compensating LTS SQUID Gradiometer Systems data. By matching the attitude changes of the pod in flight to the anomalies of the magnetic measurement data, we find that the yaw attitude changes most dramatically and corresponds best to the magnetic data anomaly interval. Based on this finding, we solved the compensation model using least squares fitting and Huber’s parametric fitting. By comparison, we found that the Huber parametric fit not only eliminates the interference introduced by attitude changes but also retains richer anomaly source information and therefore obtains a higher signal-to-noise ratio. The experimental results show that the quality of the magnetometry data obtained by using the compensation method proposed in this paper has been significantly improved, and the mean value of its improvement ratio can reach 118.93.