Robust Least Trimmed Squares (LTS) estimation is used to detect anomalies in aerodynamic data.However,the aerodynamic data are massive and high-dimensional,which makes the matrix dimension of LTS solution be very high,and leads to huge spatial and temporal cost.In this paper,Iterative Singular Value Decomposition(ISVD) is introduced to solve the least squares problem of LTS,so as to realize faster anomaly detection.In the stage of empirical analysis,the data set of the configuration of an aircraft is adopted, and OLS,FastLTS and ISVD-FastLTS are used for anomaly detection and comparison.Experimental results show that ISVD-FastLTS can identify outliers more rapidly and accurately than the traditional methods.