I am a newby to Kalmar filters, but after some study, I think I understand how it works now. For my application, I need a Kalmar filter that combines the measurement input from two sources. In the standard Kalmar filter, that is no problem at all, but it assumes that the measurement inputs from the two sensors are available at the same times. In my application, there is one new measurement from sensor 'b' for every 13 measurements of sensor 'a'.That is, 12 out of 13 times, the measurement of sensor 'b' is missing.How would you handle that normally? Do you simply use the predicted measurements values as substitute for the missing ones? Does that not lead to overconfidence in the missing measurements? How else can it be handled?

Question

I am a newby to Kalmar filters, but after some study, I think I understand how it works now. For my application, I need a Kalmar filter that combines the measurement input from two sources. In the standard Kalmar filter, that is no problem at all, but it assumes that the measurement inputs from the two sensors are available at the same times. In my application, there is one new measurement from sensor &#039;b&#039; for every 13 measurements of sensor &#039;a&#039;.That is, 12 out of 13 times, the measurement of sensor &#039;b&#039; is missing.How would you handle that normally? Do you simply use the predicted measurements values as substitute for the missing ones? Does that not lead to overconfidence in the missing measurements? How else can it be handled?

seraphinod · Accepted Answer

For a missing measurement, just use the last state estimate as a measurement but set the covariance matrix of the measurement to essentially infinity. (If the system uses inverse covariance just set the values to zero.) This would cause a Kalman filter to essentially ignore the new measurement since the ratio of the variance of the prediction to the measurement is zero. The result will be a new prediction that maintains velocity/acceleration but whose variance will grow according to the process noise.

seupeljewj · Accepted Answer

You are absolutely right. If at a time t the measurement is missing, only the time-update is computed and the measurement update must be skipped. This is the way you shold handle the problem.

I am a newby to Kalmar filters, but after some study, I think I understand how it works now. For my

Answered question

Answer & Explanation

New Questions in Pre-Algebra