Dr. XueHua Zhao | Automation | Best Researcher Award

Student, Northwestern Polytechnical University, China.

Dr. XueHua Zhao is a dedicated Chinese researcher specializing in advanced filtering algorithms, sensor networks, and non-Gaussian noise modeling. With a strong mathematical foundation and a focus on artificial intelligence in navigation systems, Dr. Zhao has published several impactful research papers in top-tier international journals and conferences. Currently pursuing a Ph.D. in Computer Science and Technology at Northwestern Polytechnical University, her contributions to maximum correntropy filtering and its distributed applications are widely recognized across the control engineering community .

Publication Profile

Scopus

Education Background

Dr. Zhao began her academic journey with a Bachelor’s degree in Mathematics Education from Henan Normal University (1998–2002) . She then pursued a Master’s degree in Computational Mathematics at Guizhou Normal University (2004–2006) . Continuing her academic advancement, she embarked on doctoral research in Computer Science and Technology at Northwestern Polytechnical University from 2016 onwards , focusing on advanced estimation and filtering techniques.

Professional Experience

With a foundation in mathematics and applied computing, Dr. Zhao has actively contributed to the scientific community through collaborative projects in signal processing and navigation systems . She has co-authored papers with experts in both academic and industrial research groups, focusing on algorithms like the Unscented Particle Filter and Sparrow Search Algorithm, highlighting her interdisciplinary approach and engineering insight .

Awards and Honors

While specific individual awards have not been explicitly mentioned, Dr. Zhao’s selection as a lead author in several high-impact journals and IEEE conferences reflects peer recognition and commendation from the academic community . Her work has drawn citations from related research in robust control, navigation systems, and sensor networks .

Research Focus

Dr. Zhao’s research interests lie at the intersection of control engineering and computational intelligence . She focuses on robust estimation methods like the Maximum Correntropy Kalman Filter (MCKF), Rational-Quadratic Kernels, and Particle Filtering under non-Gaussian and censored environments. Her work is crucial in advancing INS/GPS integrated navigation, distributed sensor fusion, and optimization algorithms for real-world uncertainty modeling and adaptive control systems .

Conclusion

Dr. XueHua Zhao continues to make meaningful contributions to control theory and intelligent filtering under uncertainty. Her deep mathematical insight, algorithmic innovation, and collaborative research spirit position her as a valuable contributor to global advancements in nonlinear filtering and smart navigation technologies .

Publication Top Notes:

Stochastic Stability of the Improved Maximum Correntropy Kalman Filter against Non-Gaussian Noises, International Journal of Control, Automation and Systems, 2024, 22(3): 731–743.
Cited by: 6 articles
Rational-Quadratic Kernel-Based Maximum Correntropy Kalman Filter for the Non-Gaussian Noises, Journal of the Franklin Institute, 2024, 361(17): 107286.
Cited by: 4 articles
Distributed Maximum Correntropy Linear Filter Based on Rational-Quadratic-kernel against Non-Gaussian Noise, Symmetry, 2025 (in press).
Cited by: Awaiting citation
A Fading Factor Unscented Particle Filter and Its Application in INS/GPS Integrated Navigation, ICISCE 2017 Proceedings, IEEE, 2017: 792–796.
Cited by: 19 articles
Adaptive Robust Unscented Particle Filter and Its Application in Sins/Sar Integration Navigation System, IAEAC 2017 Proceedings, IEEE, 2017: 2364–2368.
Cited by: 21 articles
Enhanced Sparrow Search Algorithm Based on Improved Game Predatory Mechanism and Its Application, Digital Signal Processing, 2024, 145: 104310.
Cited by: 5 articles
Linear and Nonlinear Filters Based on Statistical Similarity Measure for Sensor Network Systems, Journal of the Franklin Institute, 2025, 362(1): 107412.
Cited by: Awaiting citation
Random weighted adaptive filtering and its application in integrated navigation , Journal of Projectiles, Rockets, Missiles and Guidance , 2017 , 37(05): 1–5+10.
Cited by: 12 articles