okapi::EKFFilter Class Reference

#include <ekfFilter.hpp>

Inheritance diagram for okapi::EKFFilter:

Public Member Functions
	EKFFilter (double iQ=0.0001, double iR=ipow(0.2, 2))
	One dimensional extended Kalman filter.
double	filter (double ireading) override
	Filters a value, like a sensor reading.
virtual double	filter (double ireading, double icontrol)
	Filters a reading with a control input.
double	getOutput () const override
	Returns the previous output from filter.
Public Member Functions inherited from okapi::Filter
virtual	~Filter ()

Protected Attributes
const double	Q
const double	R
double	xHat = 0
double	xHatPrev = 0
double	xHatMinus = 0
double	P = 0
double	Pprev = 1
double	Pminus = 0
double	K = 0

Detailed Description

Definition at line 12 of file ekfFilter.hpp.

Constructor & Destructor Documentation

EKFFilter()

okapi::EKFFilter::EKFFilter ( double  iQ = 0.0001, double  iR = ipow(0.2, 2)  )

explicit

One dimensional extended Kalman filter.

The default arguments should work fine for most signal filtering. It won't hurt to graph your signal and the filtered result, and check if the filter is doing its job.

Q is the covariance of the process noise and R is the covariance of the observation noise. The default values for Q and R should be a modest balance between trust in the sensor and FIR filtering.

Think of R as how noisy your sensor is. Its value can be found mathematically by computing the standard deviation of your sensor reading vs. "truth" (of course, "truth" is still an estimate; try to calibrate your robot in a controlled setting where you can minimize the error in what "truth" is).

Think of Q as how noisy your model is. It decides how much "smoothing" the filter does and how far it lags behind the true signal. This parameter is most often used as a "tuning" parameter to adjust the response of the filter.

Parameters

iQ	process noise covariance
iR	measurement noise covariance

Member Function Documentation

filter() [1/2]

double okapi::EKFFilter::filter ( double  ireading )

overridevirtual

Filters a value, like a sensor reading.

Assumes the control input is zero.

Parameters

ireading

new measurement

Returns

filtered result

Implements okapi::Filter.

filter() [2/2]

virtual double okapi::EKFFilter::filter ( double  ireading, double  icontrol  )

virtual

Filters a reading with a control input.

Parameters

ireading	new measurement
icontrol	control input

Returns

filtered result

getOutput()

double okapi::EKFFilter::getOutput ( ) const

overridevirtual

Returns the previous output from filter.

Returns

the previous output from filter

Implements okapi::Filter.

Member Data Documentation

K

double okapi::EKFFilter::K = 0

protected

Definition at line 69 of file ekfFilter.hpp.

P

double okapi::EKFFilter::P = 0

protected

Definition at line 66 of file ekfFilter.hpp.

Pminus

double okapi::EKFFilter::Pminus = 0

protected

Definition at line 68 of file ekfFilter.hpp.

Pprev

double okapi::EKFFilter::Pprev = 1

protected

Definition at line 67 of file ekfFilter.hpp.

Q

const double okapi::EKFFilter::Q

protected

Definition at line 62 of file ekfFilter.hpp.

R

const double okapi::EKFFilter::R

protected

Definition at line 62 of file ekfFilter.hpp.

xHat

double okapi::EKFFilter::xHat = 0

protected

Definition at line 63 of file ekfFilter.hpp.

xHatMinus

double okapi::EKFFilter::xHatMinus = 0

protected

Definition at line 65 of file ekfFilter.hpp.

xHatPrev

double okapi::EKFFilter::xHatPrev = 0

protected

Definition at line 64 of file ekfFilter.hpp.

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The documentation for this class was generated from the following file:

• include/okapi/api/filter/ekfFilter.hpp