Papers
45
Total Citations
906
H-Index
17
About
Yingmin Jia is a prominent researcher whose work sits at the intersection of control theory, robotics, and distributed estimation. His scholarship spans several interconnected domains, including robust state estimation, multi-agent formation control, sensor networks, and mobile robotics, with a particular focus on developing mathematically rigorous solutions to real-world engineering challenges. Among his most influential contributions is his work on robust Kalman filtering, most notably a 2015 paper on adaptive process and measurement noise covariance estimation that has accumulated 114 citations, underscoring its broad adoption in the estimation community. His distributed consensus-based filtering frameworks — including extended Kalman filter variants for nonlinear systems over sensor networks — have further advanced decentralized state estimation, collectively drawing well over 100 additional citations. Jia has also made significant strides in multi-robot coordination, pioneering vision-based leader-follower formation control using active cameras as sole sensors, eliminating reliance on inter-robot communication. His finite-time synchronous control framework for multiple manipulators under sensor saturation and his iterative learning-based high-precision formation strategies reflect a sustained commitment to practical, high-performance robotics. With resilient filtering, variance-constrained approaches, and indoor localization techniques also among his recognized works, Jia's research portfolio represents a cohesive and impactful body of scholarship shaping modern intelligent control systems.
Research Focus
Key Achievements
Top Papers
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- 5Resilient Filtering for Nonlinear Complex Networks With Multiplicative Noise55 citations · 2018
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- 7Distributed extended Kalman filter with nonlinear consensus estimate52 citations · 2017
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