rm::KalmanFilterStaticDatas< Tp, StateDim, MeasureDim > 模板类 参考

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 $\hat{\pmb x}$
cv::Matx< Tp, StateDim, 1 >	x_
	状态的先验估计 $\hat{\pmb x}^-$
cv::Matx< Tp, MeasureDim, 1 >	z
	观测向量 $\pmb z$
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 $\hat{\pmb x}$

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 $\hat{\pmb x}^-$

z

template<typename Tp, unsigned StateDim, unsigned MeasureDim>

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

protected

观测向量 $\pmb z$

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该类的文档由以下文件生成:

• rmvl/algorithm/kalman.hpp