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About

Leszek Cedro is a researcher whose work sits at the intersection of signal processing, system identification, and robotics. His primary contributions focus on the development and application of **regressive differential filters**—a sophisticated methodology for accurately determining signal derivatives, which is critical for solving complex identification problems. Cedro’s approach excels at filtering out measurement and quantization noise, offering a robust alternative to traditional methods that require solving differential equations. His most cited work, "Determining of Signal Derivatives in Identification Problems- FIR Differential Filters" (2012, 28 citations), lays the foundation for this technique by using local polynomial approximation to estimate derivative values. He has further demonstrated the practical impact of his filters by applying them to the **parameter identification of electrically driven manipulators**, including a two-degree-of-freedom robot model (2022, 12 citations) and a three-degree-of-freedom system (2012, 8 citations). By generalizing weighted recursive least squares algorithms for nonlinear parameterization, Cedro has provided powerful tools for modeling dynamic systems, making his research invaluable for engineers and scientists working on robotics, control systems, and signal analysis.