Kalman Filter Auto Tuning
Jan 13, 2017 The Kalman filter is an algorithm that seeks to find the optimal representation for a series of observations by averaging over successive states, a type of Bayesian model. Nov 15, 2019 Kalman Filter is an estimation approach to remove noise from time series. When the Mahalanobis Distance is added to the Kalman Filter, it can become a powerful method to detect and remove outliers.
This example shows how to perform Kalman filtering. Both a steady state filter and a time varying filter are designed and simulated below.
Problem Description
Given the following discrete plant
where
design a Kalman filter to estimate the output y based on the noisy measurements yv[n] = C x[n] + v[n]
Steady-State Kalman Filter Design
You can use the function KALMAN to design a steady-state Kalman filter. This function determines the optimal steady-state filter gain M based on the process noise covariance Q and the sensor noise covariance R.
First specify the plant + noise model. CAUTION: set the sample time to -1 to mark the plant as discrete.
Specify the process noise covariance (Q):
Specify the sensor noise covariance (R):
Kalman Filter Tutorial
Now design the steady-state Kalman filter with the equations
The first output of the Kalman filter KALMF is the plant output estimate y_e = Cx[n n], and the remaining outputs are the state estimates. Keep only the first output y_e:
To see how this filter works, generate some data and compare the filtered response with the true plant response:
To simulate the system above, you can generate the response of each part separately or generate both together. To simulate each separately, first use LSIM with the plant and then with the filter. The following example simulates both together.
The piano also includes a qreat selectoin of convolutoin reverbs and convolutoin effects – and a bonus selectoin of morphinq patches (ex. Pedal volume, release volume, niose reductoin, EQ etc) and a visual velocity editor, so you can fully sculpt the piano if you will visit audiolove.club likinq. This particular sersie and era is known to be one of the best and the buildinq guality still stands unmatched – even the strinqs are still oriqinal form 1928. It is the perfect piano for soundtrack composers, sonqwriters and people lookinq for an advanced emotoinal and resonant piano. 8dio 1928 legacy steinway piano vst download. In additoin the instructent was encoded with audiolove.club two sets of microphones (close/internal) and (ambient/player), so users can adjust the sound to heir likinq ranqinq form recordinqs inside the piano to a natural player perspective.We also added a variety of controls (ex.
Next, connect the plant model and the Kalman filter in parallel by specifying u as a shared input:
Finally, connect the plant output yv to the filter input yv. Note: yv is the 4th input of SYS and also its 2nd output:
The resulting simulation model has w,v,u as inputs and y,y_e as outputs:
You are now ready to simulate the filter behavior. Generate a sinusoidal input vector (known):
Generate process noise and sensor noise vectors:
Now simulate the response using LSIM:
Compare the true response with the filtered response:
As shown in the second plot, the Kalman filter reduces the error y-yv due to measurement noise. To confirm this, compare the error covariances:
Covariance of error before filtering (measurement error):
Covariance of error after filtering (estimation error):
Time-Varying Kalman Filter Design
Now, design a time-varying Kalman filter to perform the same task. A time-varying Kalman filter can perform well even when the noise covariance is not stationary. However for this example, we will use stationary covariance.
The time varying Kalman filter has the following update equations.
First, generate the noisy plant response:
Next, implement the filter recursions in a FOR loop:
Now, compare the true response with the filtered response:
The time varying filter also estimates the output covariance during the estimation. Plot the output covariance to see if the filter has reached steady state (as we would expect with stationary input noise):
From the covariance plot you can see that the output covariance did reach a steady state in about 5 samples. From then on, the time varying filter has the same performance as the steady state version.
Compare covariance errors: Autotune free download.
Covariance of error before filtering (measurement error):
Covariance of error after filtering (estimation error):
Verify that the steady-state and final values of the Kalman gain matrices coincide: