온라인 상태 추정

명령줄과 Simulink^®에서 선형 및 비선형 칼만 필터를 사용하여 모델 파라미터 추정

실시간 데이터와 선형, 확장 또는 무향 칼만 필터 알고리즘을 사용하여 시스템의 상태를 추정할 수 있습니다. System Identification Toolbox™ 라이브러리의 추정기 하위 라이브러리에 있는 Simulink 블록을 사용하여 온라인 상태 추정을 수행할 수 있습니다. 그런 다음 Simulink Coder™를 사용하여 이들 블록에 대한 C/C++ 코드를 생성하고, 이 코드를 임베디드 타깃에 배포할 수 있습니다. 명령줄에서 온라인 상태 추정을 수행하고 MATLAB^® Compiler™ 또는 MATLAB Coder를 사용하여 코드를 배포할 수도 있습니다.

함수

`extendedKalmanFilter`	Create extended Kalman filter object for online state estimation
`unscentedKalmanFilter`	Create unscented Kalman filter object for online state estimation
`particleFilter`	Particle filter object for online state estimation
`correct`	Correct state and state estimation error covariance using extended or unscented Kalman filter, or particle filter and measurements
`residual`	Return measurement residual and residual covariance when using extended or unscented Kalman filter (R2019b 이후)
`predict`	Predict state and state estimation error covariance at next time step using extended or unscented Kalman filter, or particle filter
`initialize`	Initialize the state of the particle filter
`clone`	Copy online state estimation object
`generateJacobianFcn`	Generate MATLAB Jacobian functions for extended Kalman filter using automatic differentiation (R2023a 이후)

블록

Kalman Filter	Estimate states of discrete-time or continuous-time linear system
Extended Kalman Filter	Estimate states of discrete-time nonlinear system using extended Kalman filter
Particle Filter	Estimate states of discrete-time nonlinear system using particle filter
Unscented Kalman Filter	Estimate states of discrete-time nonlinear system using unscented Kalman filter

도움말 항목

온라인 추정 기본 사항

What Is Online Estimation?
Estimate states and parameters of a system in real-time.
Extended and Unscented Kalman Filter Algorithms for Online State Estimation
Description of the underlying algorithms for state estimation of nonlinear systems.

Simulink에서의 온라인 상태 추정

시변 칼만 필터를 사용한 상태 추정
Simulink에서 시변 칼만 필터를 사용하여 선형 시스템의 상태를 추정합니다.
Estimate States of Nonlinear System with Multiple, Multirate Sensors
Use an Extended Kalman Filter block to estimate the states of a system with multiple sensors that are operating at different sampling rates.
Validate Online State Estimation in Simulink
Validate online state estimation that is performed using Extended Kalman Filter and Unscented Kalman Filter blocks.
Parameter and State Estimation in Simulink Using Particle Filter Block
This example demonstrates the use of Particle Filter block in System Identification Toolbox™.
State Estimation with Wrapped Measurements Using Extended Kalman Filter
This example shows how to use the extended Kalman filter algorithm for nonlinear state estimation for 3D tracking involving circularly wrapped angle measurements.

명령줄에서의 온라인 상태 추정

Nonlinear State Estimation Using Unscented Kalman Filter and Particle Filter
Use the unscented Kalman filter algorithm for nonlinear state estimation for the van der Pol oscillator.
Validate Online State Estimation at the Command Line
Validate online state estimation that is performed using extended and unscented Kalman filter algorithms.
Generate Code for Online State Estimation in MATLAB
Deploy extended or unscented Kalman filters, or particle filters using MATLAB Coder software.

문제 해결

Troubleshoot Online State Estimation

Troubleshoot online state estimation performed using extended and unscented Kalman filter algorithms.