Huge amount of noise appeared in the kalman filter

Question

I am implementing a Kalman filter but I am getting a huge amount of noise to the result. The amount of noise which I added and code for it is:

y = np.arange(100)
y = y + 0.0065 * np.random.randn() # yaw rate

My resulting output is:

0.0065 is already very much less noise. Is there any way to clean this noise?

Answer 1

Kalman filter is indeed one way to filter noise out of your observations. If well implemented and tuned to the dynamics of your problem, it can effectively retrieve accurate estimates considering a Gaussian noise scenario (for different noise models the classical Kalman filter may not be suitable). As explained here , you need to tune your process and observation covariance noise matrices. Sometimes its a process of trial and error. The more noisy you consider your observations to be, the more weight the filter puts into the previous knowledge. The more noisy you consider your process to be (highly dynamic), the less weight the filter puts into the previous knowledge.