Observer-based Differentiators for Noisy Signals

TL;DR

This paper introduces observer-based differentiators capable of accurately estimating derivatives of noisy signals, enhancing signal analysis in noisy environments.

Contribution

It presents a new collection of observer-based differentiators that effectively estimate derivatives despite noise interference.

Findings

Differentiators work reliably with noisy signals

Estimates closely match true derivatives

Applicable to various noisy signal types

Abstract

We present a collection of different types of observation systems that work as differentiators. These observer-based differentiators can produce estimates for derivatives of a given signal, even though the given signal is prone to noise.