Abstract: An ARAIM subset selection method based on a BeiDou constellation includes: (a) deeming a current airspace as a two-dimensional plane and satellite distribution at some point as point distribution on the two-dimensional plane; (b) selecting coordinates of K satellites as original centroids, assigning each satellite to the closest original centroid by means of a K-Means algorithm to form K classification clusters, and recalculating a centroid of each classification cluster to obtain a centroid coordinate; (c) re-selecting the coordinates of the K satellites as the original centroids, repeating the calculation step (b) to calculate the sum of squared errors of the centroid obtained every time, and selecting the centroid with a minimum sum of squared errors as an output result; and (d) performing outlier detection on the output result of the step (c), and if an outlier appears, not performing fault assumption on a satellite at the outlier.