Defining a kalman filter and its application ( Concept) -


i have simple problem. tracking object , getting position in non-uniform time intervals. velocity , accelration of object not constant.

data_=[time x,y,z]

to design kalman filter, need define 

z=[x;y;z] % observation  % estimation vector xt=[xt;yt;zt;x't;y't;z't]  % ' first derivative    p=covariance matrix of estimation vector  r=covariance matrix of measurement  q= covariance of noise

question1: difference between these 2 r & p if measurment accuracy 1mm p? question2: benefit of using kalman filter in post processing. it smoth trajectory if yes why need it.

hope enough information people.

question 1

r covariance matrix of measurement. has nothing model , estimations.

p covariance matrix of errors in estimations. totally realted model , way estimate state. p has nothing accuracy on measurements. have compute every iteration update equations.

question 4

kalman's goal filtering noisy measurements of state want track, can result more similar real state without noise (noise uncertainty in measurements).


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