Yuriy S. Shmaliy
Papers
12
Total Citations
243
H-Index
7
About
Yuriy S. Shmaliy is a prominent researcher specializing in robust state estimation, indoor robot localization, and advanced filtering theory, with particular expertise in finite impulse response (FIR) filtering and its integration with classical Kalman-based approaches. His most influential contributions center on developing hybrid filtering frameworks — most notably combining Extended Kalman Filters (EKF) with Extended Unbiased FIR (EFIR) filters — to achieve superior accuracy and robustness in challenging indoor environments where sensor noise statistics are poorly characterized or time-varying. Shmaliy's work has made significant strides in ultra-wideband (UWB) and LiDAR-based localization systems, addressing real-world problems such as colored measurement noise, uncertain sampling periods, and persistent disturbances. His 2018 paper on UWB-based robot localization has garnered 51 citations, while multiple subsequent works each approach or exceed 47 citations, reflecting sustained community engagement with his innovations. His research extends into predictive tracking under data errors using H₂ FIR approaches, RFID-based self-localization, and biomedical signal processing for myoelectric prosthetic control. Spanning over a decade of consistent publication, Shmaliy's body of work has meaningfully advanced practical localization solutions for autonomous robotics, positioning him as a key contributor to applied estimation theory and intelligent sensing systems.
Research Focus
Key Achievements
Top Papers
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- 5UWB-Based Robot Localization Using Distributed Adaptive EFIR Filtering17 citations · 2024
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- 8Improving Gaussianity of EMG Envelope for Myoelectric Robot Arm Control3 citations · 2021
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