# 칼만 필터

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## 노트

- A Kalman filter can be used to predict the state of a system where there is a lot of input noise.
^{[1]} - The Kalman Filter estimates the true state of an object given noisy input (input with some inaccuracy).
^{[1]} - In the case of this simulation, the Kalman Filter estimates the true position of your cursor when there is random input noise.
^{[1]} - We used the Kalman Filter on an Aldebaran NAO humanoid robot as part of a class project.
^{[1]} - After reading the first part, you will be able to understand the concept of the Kalman Filter and develop the "Kalman Filter intuition".
^{[2]} - After reading the second part, you will be able to understand the math behind the Kalman Filter.
^{[2]} - Kalman filters are ideal for systems which are continuously changing.
^{[3]} - The math for implementing the Kalman filter appears pretty scary and opaque in most places you find on Google.
^{[3]} - That’s a bad state of affairs, because the Kalman filter is actually super simple and easy to understand if you look at it in the right way.
^{[3]} - The Kalman filter assumes that both variables (postion and velocity, in our case) are random and Gaussian distributed.
^{[3]} - This document gives a brief introduction to the derivation of a Kalman filter when the input is a scalar quantity.
^{[4]} - Kalman filter was proposed in the early 1960s and has been extensively used for the state estimation of dynamic systems.
^{[5]} - I would like to first explain the idea of the Kalman filter (according to Rudolf Emil Kalman ) with only one dimension .
^{[6]} - The Picture Illustrates the Kalman Filter ‘s Predition step in various time-stages.
^{[6]} - This part of the Kalman filter now dares to predict the state of the system in the future.
^{[6]} - The Kalman filter has made a prediction statement about the expected system state in the future or in the upcoming time-step.
^{[6]} - P_{k\mid k-1}} The Kalman filter keeps track of the estimated state of the system and the variance or uncertainty of the estimate.
^{[7]} - The Kalman filter also works for modeling the central nervous system's control of movement.
^{[7]} - In the prediction step, the Kalman filter produces estimates of the current state variables, along with their uncertainties.
^{[7]} - Optimality of the Kalman filter assumes that the errors are Gaussian.
^{[7]} - Both dynamic meteorological and static socioeconomic factors were selected as the vector containing controls in the Kalman filter.
^{[8]} - The HFMD incidence was the explained variable in GWR model, as well as the measurement Y in the Kalman filter.
^{[8]} - Moreover, the state vector X in the Kalman filter contains the HFMD incidence and the socioeconomic factors.
^{[8]} - The Bayesian probabilistic approach is proposed to estimate the process noise and measurement noise parameters for a Kalman filter.
^{[9]} - Before we can run the Kalman filter we must initialize the state vector.
^{[10]}

### 소스

- ↑
^{1.0}^{1.1}^{1.2}^{1.3}Kalman Filter Simulation - ↑
^{2.0}^{2.1}Kalman Filter Tutorial - ↑
^{3.0}^{3.1}^{3.2}^{3.3}How a Kalman filter works, in pictures - ↑ An introduction to scalar Kalman filters
- ↑ Kalman Filters - an overview
- ↑
^{6.0}^{6.1}^{6.2}^{6.3}The Kalman Filter: An algorithm for making sense of fused sensor insight - ↑
^{7.0}^{7.1}^{7.2}^{7.3}Kalman filter - ↑
^{8.0}^{8.1}^{8.2}Integration of a Kalman filter in the geographically weighted regression for modeling the transmission of hand, foot and mouth disease - ↑ Selection of noise parameters for Kalman filter
- ↑ Filtering data with the Kalman Filter

## 메타데이터

### 위키데이터

- ID : Q846780