Nonlinear filters | Filter theory
In signal processing, a nonlinear (or non-linear) filter is a filter whose output is not a linear function of its input. That is, if the filter outputs signals R and S for two input signals r and s separately, but does not always output αR + βS when the input is a linear combination αr + βs. Both continuous-domain and discrete-domain filters may be nonlinear. A simple example of the former would be an electrical device whose output voltage R(t) at any moment is the square of the input voltage r(t); or which is the input clipped to a fixed range [a,b], namely R(t) = max(a, min(b, r(t))). An important example of the latter is the running-median filter, such that every output sample Ri is the median of the last three input samples ri, ri−1, ri−2. Like linear filters, nonlinear filters may be shift invariant or not. Non-linear filters have many applications, especially in the removal of certain types of noise that are not additive. For example, the median filter is widely used to remove — that affects only a small percentage of the samples, possibly by very large amounts. Indeed, all radio receivers use non-linear filters to convert kilo- to gigahertz signals to the audio frequency range; and all digital signal processing depends on non-linear filters (analog-to-digital converters) to transform analog signals to binary numbers. However, nonlinear filters are considerably harder to use and design than linear ones, because the most powerful mathematical tools of signal analysis (such as the impulse response and the frequency response) cannot be used on them. Thus, for example, linear filters are often used to remove noise and distortion that was created by nonlinear processes, simply because the proper non-linear filter would be too hard to design and construct. From the foregoing, we can know that the nonlinear filters have quite different behavior compared to linear filters. The most important characteristic is that, for nonlinear filters, the filter output or response of the filter does not obey the principles outlined earlier, particularly scaling and shift invariance. Furthermore, a nonlinear filter can produce results that vary in a non-intuitive manner. (Wikipedia).
From playlist filter (less comfortable)
reaLD 3D glasses filter with a linear polarising filter
This is for a post on my blog: http://blog.stevemould.com
From playlist Everything in chronological order
Introduction to Frequency Selective Filtering
http://AllSignalProcessing.com for free e-book on frequency relationships and more great signal processing content, including concept/screenshot files, quizzes, MATLAB and data files. Separation of signals based on frequency content using lowpass, highpass, bandpass, etc filters. Filter g
From playlist Introduction to Filter Design
Why Use Kalman Filters? | Understanding Kalman Filters, Part 1
Download our Kalman Filter Virtual Lab to practice linear and extended Kalman filter design of a pendulum system with interactive exercises and animations in MATLAB and Simulink: https://bit.ly/3g5AwyS Discover common uses of Kalman filters by walking through some examples. A Kalman filte
From playlist Understanding Kalman Filters
I discuss causal and non-causal noise filters: the moving average filter and the exponentially weighted moving average. I show how to do this filtering in Excel and Python
From playlist Discrete
Special Topics - The Kalman Filter (1 of 55) What is a Kalman Filter?
Visit http://ilectureonline.com for more math and science lectures! In this video I will explain what is Kalman filter and how is it used. Next video in this series can be seen at: https://youtu.be/tk3OJjKTDnQ
From playlist SPECIAL TOPICS 1 - THE KALMAN FILTER
Nonlinear State Estimators | Understanding Kalman Filters, Part 5
Download our Kalman Filter Virtual Lab to practice linear and extended Kalman filter design of a pendulum system with interactive exercises and animations in MATLAB and Simulink: https://bit.ly/3g5AwyS This video explains the basic concepts behind nonlinear state estimators, including ext
From playlist Understanding Kalman Filters
http://AllSignalProcessing.com for more great signal processing content, including concept/screenshot files, quizzes, MATLAB and data files. Noncausal filtering of stored data to obtain zero-phase response using the time-reversal property of the DFT, as implemented by the "filtfilt" comma
From playlist Introduction to Filter Design
C52 Introduction to nonlinear DEs
A first look at nonlinear differential equations. In this first video examples are shown of equations that still have explicit solutions.
From playlist Differential Equations
卡尔曼滤波器是一种优化估算算法,在不确定和间接测量的情况下估算系统状态。 观看视频示例,了解卡尔曼滤波器背后的工作原理。本视频解释了非线性状态估算器背后的基本概念,包括扩展卡尔曼滤波器,无味卡尔曼滤波器和粒子滤波器。 使用 MATLAB 和 Simulink 设计和使用卡尔曼滤波器:https://bit.ly/2GXwjxG 了解 Control System Toolbox:https://bit.ly/2BWJECb 获取免费试用版,30 天探索触手可及:https://bit.ly/2IPvqcc 观看更多 MATLAB 和 Simulink 入门视频:http
From playlist 卡尔曼滤波器(Kalman Filters)
How to Use an Extended Kalman Filter in Simulink | Understanding Kalman Filters, Part 7
Download our Kalman Filter Virtual Lab to practice linear and extended Kalman filter design of a pendulum system with interactive exercises and animations in MATLAB and Simulink: https://bit.ly/3g5AwyS This video demonstrates how you can estimate the angular position of a nonlinear pendul
From playlist Understanding Kalman Filters
Lec 15 | MIT RES.6-008 Digital Signal Processing, 1975
Lecture 15: Design of IIR digital filters, part 2 Instructor: Alan V. Oppenheim View the complete course: http://ocw.mit.edu/RES6-008S11 License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
From playlist MIT RES.6-008 Digital Signal Processing, 1975
Gaussian approximations in smoothers and filters... - Morzfeld - Workshop 2 - CEB T3 2019
Morzfeld (U Arizona, USA) / 13.11.2019 Gaussian approximations in smoothers and filters for data assimilation ---------------------------------- Vous pouvez nous rejoindre sur les réseaux sociaux pour suivre nos actualités. Facebook : https://www.facebook.com/InstitutHenriPoincar
From playlist 2019 - T3 - The Mathematics of Climate and the Environment
Kalman filtering - Lakshmivarahan
PROGRAM: Data Assimilation Research Program Venue: Centre for Applicable Mathematics-TIFR and Indian Institute of Science Dates: 04 - 23 July, 2011 DESCRIPTION: Data assimilation (DA) is a powerful and versatile method for combining observational data of a system with its dynamical mod
From playlist Data Assimilation Research Program
Data Driven Methods for Complex Turbulent Systems ( 3 ) - Andrew J. Majda
Lecture 3: Data Driven Methods for Complex Turbulent Systems Abstract: An important contemporary research topic is the development of physics constrained data driven methods for complex, large-dimensional turbulent systems such as the equations for climate change science. Three new approa
From playlist Mathematical Perspectives on Clouds, Climate, and Tropical Meteorology
Stability of the optimal nonlinear filter - Oljaca - Workshop 2 - CEB T3 2019
Oljaca (U Reading, UK) / 15.11.2019 Stability of the optimal nonlinear filter ---------------------------------- Vous pouvez nous rejoindre sur les réseaux sociaux pour suivre nos actualités. Facebook : https://www.facebook.com/InstitutHenriPoincare/ Twitter : https://twitter.com
From playlist 2019 - T3 - The Mathematics of Climate and the Environment
Asymptotic properties of Kalman filter by Amit Apte
Indian Statistical Physics Community Meeting 2016 URL: https://www.icts.res.in/discussion_meeting/details/31/ DATES Friday 12 Feb, 2016 - Sunday 14 Feb, 2016 VENUE Ramanujan Lecture Hall, ICTS Bangalore This is an annual discussion meeting of the Indian statistical physics community wh
From playlist Indian Statistical Physics Community Meeting 2016
Get a Free Trial: https://goo.gl/C2Y9A5 Get Pricing Info: https://goo.gl/kDvGHt Ready to Buy: https://goo.gl/vsIeA5 Remove an unwanted tone from a signal, and compensate for the delay introduced in the process using Signal Processing Toolbox™. For more on Signal Processing Toolbox, visi
From playlist Signal Processing and Communications
Chris Jones - Does the problem matter
PROGRAM: Nonlinear filtering and data assimilation DATES: Wednesday 08 Jan, 2014 - Saturday 11 Jan, 2014 VENUE: ICTS-TIFR, IISc Campus, Bangalore LINK:http://www.icts.res.in/discussion_meeting/NFDA2014/ The applications of the framework of filtering theory to the problem of data assimi
From playlist Nonlinear filtering and data assimilation