状态估计

设计卡尔曼滤波器，估计系统运行期间的状态，生成代码和部署到嵌入式目标

您可以使用状态估计方法估计具有过程噪声和测量噪声的系统中的状态值。您可以使用 Control System Toolbox™ 提供的工具来设计线性稳态和时变卡尔曼滤波器。还可以使用扩展卡尔曼滤波器、无迹卡尔曼滤波器或粒子滤波器估计非线性系统的状态。

当有新数据可用时，在线状态估计算法会更新系统的状态估计。您可以使用实时数据以及线性和非线性卡尔曼滤波器算法来估计系统的状态。您可以使用 Simulink^® 模块执行在线状态估计，使用 Simulink Coder™ 为这些模块生成 C/C++ 代码，并将这些代码部署到嵌入式目标。还可以在命令行中执行在线状态估计，并使用 MATLAB^® Compiler™ 或 MATLAB Coder 部署您的代码。

函数

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卡尔曼滤波器设计

`kalman`	Design Kalman filter for state estimation
`kalmd`	Design discrete Kalman estimator for continuous plant
`estim`	Form state estimator given estimator gain

在线状态估计

`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
`predict`	Predict state and state estimation error covariance at next time step using extended or unscented Kalman filter, or particle filter
`residual`	Return measurement residual and residual covariance when using extended or unscented Kalman filter (自 R2019b 起)
`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 (自 R2023a 起)

模块

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Simulink 中的状态估计

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

主题

状态估计基础知识

卡尔曼滤波
执行卡尔曼滤波并仿真系统，以显示滤波器如何减少稳态和时变滤波器的测量误差。

在线估计

Nonlinear State Estimation Using Unscented Kalman Filter and Particle Filter
Estimate nonlinear states of a van der Pol oscillator using the unscented Kalman filter algorithm.
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.
Extended and Unscented Kalman Filter Algorithms for Online State Estimation
Description of the underlying algorithms for state estimation of nonlinear systems.

Simulink 中的状态估计

State Estimation Using Time-Varying Kalman Filter
Estimate the states of linear systems using time-varying Kalman filters in Simulink.
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.
Parameter and State Estimation in Simulink Using Particle Filter Block
This example demonstrates the use of Particle Filter block in Control System 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.
Nonlinear State Estimation of a Degrading Battery System
Estimate the states of a nonlinear system using an unscented Kalman filter in Simulink.
Validate Online State Estimation in Simulink
Validate online state estimation that is performed using Extended Kalman Filter and Unscented Kalman Filter blocks.