Real-time processing can reduce the pressure of data storage and downlink transmission caused by the ever-expending hyperspectral dataset, which has received more and more attention in hyperspectral anomaly detection. Since acquiring data with pushbroom has become main stream for hyperspectral imaging sensors, a real-time anomaly target detection method is proposed based on the framework of progressive line processing. In order to make sure the causality of real-time processing, the local causal window model is introduced into the Reed-Xiaoli anomaly detection algorithm, and the sliding local causal window is used to detect anomaly targets. In terms of the high computational complexity caused by the inversion of matrix, the recursive principle of the Kalman filter and the Woodbury′s lemma are employed to update the status information of current data through iterating data status information at the previous moment, which avoids the inversion of large matrix. The simulated and real hyperspectral data are adopted for the experiment. The results show that under the premise of maintaining the detection accuracy, the proposed real-time algorithm improves the processing efficiency significantly compared with the original algorithm.