| RMVL 2.4.0 Robotic Manipulation and Vision Library

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Public 成员函数 | Protected 属性 | 所有成员列表
rm::KalmanFilterStaticDatas< Tp, StateDim, MeasureDim > 模板类 参考
数学、算法模块 » 数据与信号处理 » 卡尔曼滤波模块

卡尔曼滤波静态数据 更多...

`#include <rmvl/algorithm/kalman.hpp>`

类 rm::KalmanFilterStaticDatas< Tp, StateDim, MeasureDim > 继承关系图:
rm::KalmanFilterStaticDatas< Tp, StateDim, MeasureDim > 的协作图:

## Public 成员函数
| KalmanFilterStaticDatas ()
| 构造新的卡尔曼滤波静态数据
void | init (const cv::Matx< Tp, StateDim, 1 > &x0, Tp error)
| 初始化状态以及对应的误差协方差矩阵（常数对角矩阵）
void | init (const cv::Matx< Tp, StateDim, 1 > &x0, const cv::Matx< Tp, StateDim, 1 > &error)
| 初始化状态以及对应的误差协方差矩阵（对角矩阵）
void | setR (const cv::Matx< Tp, MeasureDim, MeasureDim > &measure_err)
| 设置测量噪声协方差矩阵 \(R\)
void | setQ (const cv::Matx< Tp, StateDim, StateDim > &process_err)
| 设置过程噪声协方差矩阵 \(Q\)
void | setP (const cv::Matx< Tp, StateDim, StateDim > &state_err)
| 设置误差协方差矩阵 \(P\)

## Protected 属性
cv::Matx< Tp, StateDim, 1 > | x
| 状态的后验估计 \(\hat{\boldsymbol x}\)
cv::Matx< Tp, StateDim, 1 > | x_
| 状态的先验估计 \(\hat{\boldsymbol x}^-\)
cv::Matx< Tp, MeasureDim, 1 > | z
| 观测向量 \(\boldsymbol z\)
cv::Matx< Tp, StateDim, StateDim > | Q
| 过程噪声协方差矩阵 \(Q\)
cv::Matx< Tp, MeasureDim, MeasureDim > | R
| 测量噪声协方差矩阵 \(R\)
cv::Matx< Tp, StateDim, StateDim > | P
| 后验误差协方差矩阵 \(P\)
cv::Matx< Tp, StateDim, StateDim > | P_
| 先验误差协方差矩阵 \(P^-\)
cv::Matx< Tp, StateDim, StateDim > | I
| 单位矩阵 \(I\)
cv::Matx< Tp, StateDim, MeasureDim > | K
| 卡尔曼增益 \(K\)

## 详细描述

template<typename Tp, unsigned StateDim, unsigned MeasureDim>
class rm::KalmanFilterStaticDatas< Tp, StateDim, MeasureDim >

卡尔曼滤波静态数据

模板参数

  Tp | 数据类型
  StateDim | 状态量个数
  MeasureDim | 观测量个数

## 构造及析构函数说明

## ◆ KalmanFilterStaticDatas()

template<typename Tp, unsigned StateDim, unsigned MeasureDim>

rm::KalmanFilterStaticDatas< Tp, StateDim, MeasureDim >::KalmanFilterStaticDatas | ( |  | ) |
| inline

构造新的卡尔曼滤波静态数据

## 成员函数说明

## ◆ init() [1/2]

template<typename Tp, unsigned StateDim, unsigned MeasureDim>

void rm::KalmanFilterStaticDatas< Tp, StateDim, MeasureDim >::init | ( | const cv::Matx< Tp, StateDim, 1 > & | x0,
|  | const cv::Matx< Tp, StateDim, 1 > & | error )
| inline

初始化状态以及对应的误差协方差矩阵（对角矩阵）

参数

  [in] | x0 | 初始化的状态向量
  [in] | error | 状态误差矩阵的对角线元素

函数调用图:

## ◆ init() [2/2]

template<typename Tp, unsigned StateDim, unsigned MeasureDim>

void rm::KalmanFilterStaticDatas< Tp, StateDim, MeasureDim >::init | ( | const cv::Matx< Tp, StateDim, 1 > & | x0,
|  | Tp | error )
| inline

初始化状态以及对应的误差协方差矩阵（常数对角矩阵）

参数

  [in] | x0 | 初始化的状态向量
  [in] | error | 状态误差系数

函数调用图:

## ◆ setP()

template<typename Tp, unsigned StateDim, unsigned MeasureDim>

void rm::KalmanFilterStaticDatas< Tp, StateDim, MeasureDim >::setP | ( | const cv::Matx< Tp, StateDim, StateDim > & | state_err | ) |
| inline

设置误差协方差矩阵 \(P\)

参数

  [in] | state_err | 误差协方差矩阵 \(P\)

## ◆ setQ()

template<typename Tp, unsigned StateDim, unsigned MeasureDim>

void rm::KalmanFilterStaticDatas< Tp, StateDim, MeasureDim >::setQ | ( | const cv::Matx< Tp, StateDim, StateDim > & | process_err | ) |
| inline

设置过程噪声协方差矩阵 \(Q\)

参数

  [in] | process_err | 过程噪声协方差矩阵 \(Q\)

## ◆ setR()

template<typename Tp, unsigned StateDim, unsigned MeasureDim>

void rm::KalmanFilterStaticDatas< Tp, StateDim, MeasureDim >::setR | ( | const cv::Matx< Tp, MeasureDim, MeasureDim > & | measure_err | ) |
| inline

设置测量噪声协方差矩阵 \(R\)

参数

  [in] | measure_err | 测量噪声协方差矩阵 \(R\)

## 类成员变量说明

## ◆ I

template<typename Tp, unsigned StateDim, unsigned MeasureDim>

cv::Matx<Tp, StateDim, StateDim> rm::KalmanFilterStaticDatas< Tp, StateDim, MeasureDim >::I
| protected

单位矩阵 \(I\)

## ◆ K

template<typename Tp, unsigned StateDim, unsigned MeasureDim>

cv::Matx<Tp, StateDim, MeasureDim> rm::KalmanFilterStaticDatas< Tp, StateDim, MeasureDim >::K
| protected

卡尔曼增益 \(K\)

## ◆ P

template<typename Tp, unsigned StateDim, unsigned MeasureDim>

cv::Matx<Tp, StateDim, StateDim> rm::KalmanFilterStaticDatas< Tp, StateDim, MeasureDim >::P
| protected

后验误差协方差矩阵 \(P\)

## ◆ P_

template<typename Tp, unsigned StateDim, unsigned MeasureDim>

cv::Matx<Tp, StateDim, StateDim> rm::KalmanFilterStaticDatas< Tp, StateDim, MeasureDim >::P_
| protected

先验误差协方差矩阵 \(P^-\)

## ◆ Q

template<typename Tp, unsigned StateDim, unsigned MeasureDim>

cv::Matx<Tp, StateDim, StateDim> rm::KalmanFilterStaticDatas< Tp, StateDim, MeasureDim >::Q
| protected

过程噪声协方差矩阵 \(Q\)

## ◆ R

template<typename Tp, unsigned StateDim, unsigned MeasureDim>

cv::Matx<Tp, MeasureDim, MeasureDim> rm::KalmanFilterStaticDatas< Tp, StateDim, MeasureDim >::R
| protected

测量噪声协方差矩阵 \(R\)

## ◆ x

template<typename Tp, unsigned StateDim, unsigned MeasureDim>

cv::Matx<Tp, StateDim, 1> rm::KalmanFilterStaticDatas< Tp, StateDim, MeasureDim >::x
| protected

状态的后验估计 \(\hat{\boldsymbol x}\)

## ◆ x_

template<typename Tp, unsigned StateDim, unsigned MeasureDim>

cv::Matx<Tp, StateDim, 1> rm::KalmanFilterStaticDatas< Tp, StateDim, MeasureDim >::x_
| protected

状态的先验估计 \(\hat{\boldsymbol x}^-\)

## ◆ z

template<typename Tp, unsigned StateDim, unsigned MeasureDim>

cv::Matx<Tp, MeasureDim, 1> rm::KalmanFilterStaticDatas< Tp, StateDim, MeasureDim >::z
| protected

观测向量 \(\boldsymbol z\)