error_control.m File Reference

Functions
Plot Error Covariance hold on	title ('Residue error covariance(window size 15)')
Plot Error Covariance hold on	xlabel ('Time(normalised)')
Plot Error Covariance hold on	ylabel ('Covariance') plot(t
Plot Error Covariance hold on cov_residue_x	plot (t, cov_residue_y,'r') legend('X'
Plot Error Covariance hold on cov_residue_x Y hold on	title ('Innovation error covariance(window size 15)')
Plot Error Covariance hold on cov_residue_x Y hold on cov_innovation_x	plot (t, cov_innovation_y,'r') legend('X'
Plot Error Covariance hold on cov_residue_x Y hold on cov_innovation_x Y Plot Erro hold on	title ('Residue error')
Plot Error Covariance hold on cov_residue_x Y hold on cov_innovation_x Y Plot Erro hold on	ylabel ('Error/mm') plot(t
Plot Error Covariance hold on cov_residue_x Y hold on cov_innovation_x Y Plot Erro hold on residue_x	plot (t, residue_y,'r') mean_residue_x
	legend (xL, yL,'Location','Best') figure
hold on	title ('Innovation error')
hold on innovation_x	plot (t, innovation_y,'r') mean_innovation_x
Variables
Kalman performance estimation via residue and innovation error Read data clear	all
clc	cE = dlmread('covariance_residue_innovation')
	E = dlmread('residue_innovation')
	time_lenght = 200
	cov_residue_x = cE(1:time_lenght,3)
	cov_residue_y = cE(1:time_lenght,4)
	cov_innovation_x = cE(1:time_lenght,1)
	cov_innovation_y = cE(1:time_lenght,2)
	residue_x = E(1:time_lenght,3)
	residue_y = E(1:time_lenght,4)
	innovation_x = E(1:time_lenght,1)
	innovation_y = E(1:time_lenght,2)
	t = linspace(0,1,time_lenght)
Plot Error Covariance	figure
	std_residue_x = std(residue_x)
	mean_residue_y = mean(residue_y)
	std_residue_y = std(residue_y)
	xL = sprintf('X mean %g std %g',mean_residue_x,std_residue_x)
	yL = sprintf('Y mean %g std %g',mean_residue_y,std_residue_y)
	std_innovation_x = std(innovation_x)
	mean_innovation_y = mean(innovation_y)
	std_innovation_y = std(innovation_y)

---

Function Documentation

legend	(	xL	,
		yL	,
		'Location'	,
		'Best'
	)

hold on innovation_x plot	(	t	,
		innovation_y	,
		'r'
	)

Plot Error Covariance hold on cov_residue_x Y hold on cov_innovation_x Y Plot Erro hold on residue_x plot	(	t	,
		residue_y	,
		'r'
	)

Plot Error Covariance hold on cov_residue_x Y hold on cov_innovation_x plot	(	t	,
		cov_innovation_y	,
		'r'
	)

Plot Error Covariance hold on cov_residue_x plot	(	t	,
		cov_residue_y	,
		'r'
	)

hold on title

(

'Innovation error'

)

Plot Error Covariance hold on cov_residue_x Y hold on cov_innovation_x Y Plot Erro hold on title

(

'Residue error'

)

Type Constraints

Plot Error Covariance hold on cov_residue_x Y hold on title

(

'Innovation error covariance(window size 15)'

)

Type Constraints

Plot Error Covariance hold on title

(

'Residue error covariance(window size 15)'

)

Type Constraints

Plot Error Covariance hold on xlabel

(

'Time(normalised)'

)

Plot Error Covariance hold on cov_residue_x Y hold on cov_innovation_x Y Plot Erro hold on ylabel

(

'Error/mm'

)

Plot Error Covariance hold on ylabel

(

'Covariance'

)

---

Variable Documentation

close all

clc cE = dlmread('covariance_residue_innovation')

cov_innovation_x = cE(1:time_lenght,1)

cov_innovation_y = cE(1:time_lenght,2)

cov_residue_x = cE(1:time_lenght,3)

cov_residue_y = cE(1:time_lenght,4)

E = dlmread('residue_innovation')

Plot Error Covariance hold on cov_residue_x Y hold on cov_innovation_x Y Plot Erro figure

innovation_x = E(1:time_lenght,1)

innovation_y = E(1:time_lenght,2)

mean_innovation_y = mean(innovation_y)

mean_residue_y = mean(residue_y)

residue_x = E(1:time_lenght,3)

residue_y = E(1:time_lenght,4)

std_innovation_x = std(innovation_x)

std_innovation_y = std(innovation_y)

std_residue_x = std(residue_x)

std_residue_y = std(residue_y)

t = linspace(0,1,time_lenght)

time_lenght = 200

xL = sprintf('X mean %g std %g',mean_residue_x,std_residue_x)

yL = sprintf('Y mean %g std %g',mean_residue_y,std_residue_y)