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A generalizedcalibration technique for model errors is proposed for uniform linear arrays. Being different from the existing calibration methods, this technique constructs covariance matrices for two adjacent sampling periods, from a reference source with a known location, and then estimates the model errors from the difference between these matrices and an equation formed by using the MUSIC null space. The proposed technique can not only calibrate different types of model errors (such as gain-phase errors, geometry errors and mutual coupling) in different impinging source scenarios, but also has great robustness to different types of background noise. Meanwhile, it is computationally efficient and does not require an accurate clock synchronization between the reference source and the array receiving system. However, it requires that the statistical properties of both signal and noise must be constant in the two adjacent sampling periods. Numerical examples validate the superiority and effectiveness of the proposed technique.