Lingji Chen
Papers
1
Total Citations
11
H-Index
1
About
Lingji Chen’s research centers on resource-constrained state estimation, real-time control systems, and the intersection of embedded computing with estimation theory. His most notable contribution is the development of the Any-Time Kalman Filter (AKF), a framework that optimally selects measurements when processing resources—such as CPU time and memory—are limited and shared among competing tasks. In his seminal 2009 paper, “Optimal measurement selection for Any-time Kalman Filtering with processing constraints” (11 citations), Chen addressed how task interference from preemption and blocking degrades estimation accuracy, proposing a method to dynamically choose which measurements to process to maintain real-time performance. This work is foundational for embedded systems where reliable state estimation must coexist with other computational demands. Chen’s research has practical implications for autonomous vehicles, robotics, and aerospace systems, where timely and accurate filtering is critical despite hardware constraints. Though his citation count is modest, his ideas on anytime algorithms and resource-aware estimation have influenced subsequent work in adaptive filtering and real-time control. His contributions continue to guide engineers designing systems that must balance estimation fidelity with limited processing capacity.
Research Focus
Key Achievements
Top Papers
- 1