**5. Conclusions**

In this paper, a novel DSE method was proposed based on the EnSRF; a simplified Sage–Husa adaptive Kalman filter with relatively simple principle and good practicability was introduced. In the proposed AEnSRF method, set members were utilized to approximate the posterior distribution of the real state without random disturbance to the measured values. Comparing with EnKF, AEnSRF could enhance the accuracy of filtering. At the same time, Sage–Husa noise estimator was added between the prediction and correction steps to estimate the measurement noise online and in real time, which effectively avoided the problem that the filtering-precision was reduced or even divergent due to the deviation of the measurement noise.

**Author Contributions:** D.N. created models, developed methodology, wrote the initial draft, and designed computer programs; W.W. supervised and was responsible for leading the research activity planning and presented critical review; K.W. conducted research and investigation process and edited the initial draft. D.N., W.W., R.J.M., H.H.A., and P.S. reviewed the manuscript and synthesized study data. All authors read and approved the manuscript.

**Funding:** This work was supported by the National Natural Science Foundation of China under Grant 51667020.

**Conflicts of Interest:** The authors declare no conflict of interest.
