Stephen Alexander Chee

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About

Stephen Alexander Chee is a researcher whose work bridges the critical intersection of estimation theory, optimization, and robotics. His primary research areas include constrained state estimation, linear matrix inequalities (LMIs), and autonomous systems planning. Chee’s most significant contribution lies in advancing state estimation for nonlinear systems under complex constraints—a fundamental challenge in control and robotics. His landmark 2019 paper, “Linear- and Linear-Matrix-Inequality-Constrained State Estimation for Nonlinear Systems,” which has garnered 17 citations, introduced a novel approach by reformulating the Kalman filter’s maximum likelihood objective to solve for the gain through constrained optimization. This work, alongside his 2017 paper on norm- and linear-inequality-constrained estimation using LMIs, provides a rigorous framework for ensuring stability and accuracy in systems where physical or safety constraints must be respected. Beyond estimation, Chee’s earlier research on opportunistic planning for transportation robot fleets (2001, 10 citations) demonstrates a long-standing interest in practical, multi-agent robotic systems. His work is particularly valuable for students and engineers working on autonomous vehicles, sensor fusion, and safety-critical control, offering mathematically elegant solutions to real-world constraint problems.