2. 2 Introduction to Kalman Filters Michael Williams 5 June 2003

3. 3 Overview The Problem – Why do we need Kalman Filters? What is a Kalman Filter? Conceptual Overview The Theory of Kalman Filter Simple Example

4. 4 The Problem System state cannot be measured directly Need to estimate “optimally” from measurements Measuring Devices Estimator Measurement Error Sources System State (desired but not known) External Controls Observed Measurements Optimal Estimate of System State System Error Sources System Black Box

5. 5 What is a Kalman Filter? Recursive data processing algorithm Generates optimal estimate of desired quantities given the set of measurements Optimal? –For linear system and white Gaussian errors, Kalman filter is “best” estimate based on all previous measurements –For non-linear system optimality is ‘qualified’ Recursive? –Doesn’t need to store all previous measurements and reprocess all data each time step

6. 6 Conceptual Overview Simple example to motivate the workings of the Kalman Filter Theoretical Justification to come later – for now just focus on the concept Important: Prediction and Correction

7. 7 Conceptual Overview Lost on the 1-dimensional line Position – y(t) Assume Gaussian distributed measurements y

8. 8 Conceptual Overview Sextant Measurement at t 1 : Mean = z 1 and Variance =  z1 Optimal estimate of position is: ŷ(t 1 ) = z 1 Variance of error in estimate:  2 x (t 1 ) =  2 z1 Boat in same position at time t 2 - Predicted position is z 1

9. 9 Conceptual Overview So we have the prediction ŷ - (t 2 ) GPS Measurement at t 2 : Mean = z 2 and Variance =  z2 Need to correct the prediction due to measurement to get ŷ(t 2 ) Closer to more trusted measurement – linear interpolation? prediction ŷ - (t 2 ) measurement z(t 2 )

10. 10 Conceptual Overview Corrected mean is the new optimal estimate of position New variance is smaller than either of the previous two variances measurement z(t 2 ) corrected optimal estimate ŷ(t 2 ) prediction ŷ - (t 2 )

11. 11 Conceptual Overview Lessons so far: Make prediction based on previous data - ŷ -,  - Take measurement – z k,  z Optimal estimate (ŷ) = Prediction + (Kalman Gain) * (Measurement - Prediction) Variance of estimate = Variance of prediction * (1 – Kalman Gain)

12. 12 Conceptual Overview At time t 3, boat moves with velocity dy/dt=u Naïve approach: Shift probability to the right to predict This would work if we knew the velocity exactly (perfect model) ŷ(t 2 ) Naïve Prediction ŷ - (t 3 )

13. 13 Conceptual Overview Better to assume imperfect model by adding Gaussian noise dy/dt = u + w Distribution for prediction moves and spreads out ŷ(t 2 ) Naïve Prediction ŷ - (t 3 ) Prediction ŷ - (t 3 )

14. 14 Conceptual Overview Now we take a measurement at t 3 Need to once again correct the prediction Same as before Prediction ŷ - (t 3 ) Measurement z(t 3 ) Corrected optimal estimate ŷ(t 3 )

15. 15 Conceptual Overview Lessons learnt from conceptual overview: –Initial conditions (ŷ k-1 and  k-1 ) –Prediction (ŷ - k,  - k ) Use initial conditions and model (eg. constant velocity) to make prediction – Measurement (z k ) Take measurement –Correction (ŷ k,  k ) Use measurement to correct prediction by ‘blending’ prediction and residual – always a case of merging only two Gaussians Optimal estimate with smaller variance

16. Major equation 16

17. 17 Theoretical Basis Process to be estimated: y k = Ay k-1 + Bu k + w k-1 z k = Hy k + v k Process Noise (w) with covariance Q Measurement Noise (v) with covariance R Kalman Filter Predicted: ŷ - k is estimate based on measurements at previous time-steps ŷ k = ŷ - k + K(z k - H ŷ - k ) Corrected: ŷ k has additional information – the measurement at time k K = P - k H T (HP - k H T + R) -1 ŷ - k = Ay k-1 + Bu k P - k = AP k-1 A T + Q P k = (I - KH)P - k

18. 18 Blending Factor If we are sure about measurements: –Measurement error covariance (R) decreases to zero –K decreases and weights residual more heavily than prediction If we are sure about prediction –Prediction error covariance P - k decreases to zero –K increases and weights prediction more heavily than residual

19. 19 Theoretical Basis ŷ - k = Ay k-1 + Bu k P - k = AP k-1 A T + Q Prediction (Time Update) (1) Project the state ahead (2) Project the error covariance ahead Correction (Measurement Update) (1) Compute the Kalman Gain (2) Update estimate with measurement z k (3) Update Error Covariance ŷ k = ŷ - k + K(z k - H ŷ - k ) K = P - k H T (HP - k H T + R) -1 P k = (I - KH)P - k

20. 20 Quick Example – Constant Model Measuring Devices Estimator Measurement Error Sources System State External Controls Observed Measurements Optimal Estimate of System State System Error Sources System Black Box

21. A simple example Estimate a random constant:” voltage” reading from a source. It has a constant value of aV (volts), so there is no control signal u k. Standard deviation of the measurement noise is 0.1 V. It is a 1 dimensional signal problem: A and H are constant 1. Assume the error covariance P 0 is initially 1 and initial state X 0 is 0. 21

22. A simple example – Part 1 Time12345678910 Value0.390.500.480.290.250.320.340.480.410.45 22

23. A simple example – Part 2 23

24. A simple example – Part 3 24

25. Result of the example 25

26. 26 References 1.Kalman, R. E. 1960. “A New Approach to Linear Filtering and Prediction Problems”, Transaction of the ASME-- Journal of Basic Engineering, pp. 35-45 (March 1960). 2.Maybeck, P. S. 1979. “Stochastic Models, Estimation, and Control, Volume 1”, Academic Press, Inc. 3.Welch, G and Bishop, G. 2001. “An introduction to the Kalman Filter”, http://www.cs.unc.edu/~welch/kalman/