To solve the problem that the eigenvectors corresponding to the mainlobe interference are difficult to distinguish in eigen-projection matrix preprocessing, a mainlobe interference suppression algorithm based on subspace orthogonality is proposed.Firstly, the prior information of rough mainlobe region is exploited to calculate the mainlobe subspace, and eigen decomposition is made to the sampling covariance matrix.Then, the orthogonality of the obtained eigenvectors of interference is tested one by one in the mainlobe subspace, and the eigenvectors corresponding to the mainlobe interference are extracted.Subsequently, the mainlobe component can be suppressed by eigen-projection matrix preprocessing.Finally, the adaptive weight vector can be calculated by covariance matrix reconstruction to cancel sidelobe interference.Simulation results validate that, the subspace orthogonality test can detect all the eigenvectors corresponding to the mainlobe interference, suppress multiple mainlobe interferences successfully, and solve the problem that the mainlobe interference suppression method based on eigen-projection matrix preprocessing and covariance matrix reconstruction can only suppress single mainlobe interference.