2. State-Space Recursive Least Squares with Adaptive Memory College of Electrical & Mechanical Engineering National University of Sciences & Technology (NUST) EE-869 Adaptive Filters

3. 3 Introduction A recursive algorithm Built around state-space model of an unforced system Based on least squares approach Does not require process or observation noise statistics Works for time-invariant & time-variant environment alike Can handle scalar and vector observations Adapts forgetting factor that may be required due to  Model uncertainty  Presence of unknown external disturbances  Time-varying nature of observed signal  Non-stationary behaviour of observation noise SSRLS SSRLSWAM

4. Preview of SSRLS

5. 5 State-Space Model process states output signal observation noise system matrix (full rank) observation matrix (full rank) L-step observable Unforced System SSRLS

6. 6 Batch Processed Least Squares Approach

7. 7 Batch of Observations Batch Processed Least Squares Approach Noise Vector

8. 8 Least Squares Solution Batch Processed Least Squares Approach Full rank for Batch Processed Least Squares Solution Batch Processed Weighted Least Squares Solution Weighting Matrix

9. 9 Recursive Algorithm

10. 10 Predict and Correct Recursive Algorithm Predicted States Predicted Signal Prediction Error Predictor Corrector Form Estimator Gain

11. 11 Recursive Solution Recursive Algorithm Based on k+1 observations Weighting Matrix k+1 observations

12. 12 Recursive Solution (‘contd) Recursive Algorithm Defined variables Direct Form of SSRLS

13. 13 Recursive Update of Recursive Algorithm Difference Lyapunov Equation

14. 14 Matrix Inversion Lemma Recursive Algorithm Matrix Inversion Lemma

15. 15 Recursive Update of Recursive Algorithm Riccati Equation for SSRLS Define

16. 16 Recursive Update of Recursive Algorithm Recursive solution

17. 17 Observer Gain Recursive Algorithm Defined

18. 18 State-Space Representation of SSRLS Recursive Solution Defined Therefore Similarly State-Space Matrices

19. 19 Initializating SSRLS Recursive Algorithm Rank Deficient or 1) Initializing using Regularization Term 2) Initialization using batch processing approach leads to delayed recursion -  offers better initialization

20. 20 Steady-State SSRLS

21. 21 Steady-State Solution of SSRLS Steady-State SSRLS if Can be written like this For neutrally stable systems

22. 22 Direct Form of Steady-State SSRLS Steady-State SSRLS

23. 23 Observer Gain for Steady-State SSRLS Steady-State SSRLS

24. 24 Transfer Function Representation Steady-State SSRLS

25. 25 Initialization of Steady-State SSRLS Steady-State SSRLS Initialize only Preferable choice if no other estimate is available

26. 26 Memory Length Steady-State SSRLS Filter Memory Asymptotic result

27. Model Uncertainty and Unknown External Disturbances

28. 28 Underlying Model process states output external disturbance (bounded, deterministic) observation noise system matrix input matrix observation matrix Model Uncertainty and Unknown External Disturbances Controllable pair

29. 29 Assumptions about Observation Noise Zero Mean White Model Uncertainty and Unknown External Disturbances

30. 30 Perturbation Matrices Model Uncertainty and Unknown External Disturbances

31. 31 Estimation Error where Model Uncertainty and Unknown External Disturbances White Input Deterministic Input

32. 32 Steady-State Mean Estimation Error Model Uncertainty and Unknown External Disturbances Deterministic Input

33. 33 Bounds on Steady-State Mean Estimation Error Model Uncertainty and Unknown External Disturbances

34. 34 Steady-State Mean Square Error Model Uncertainty and Unknown External Disturbances Estimation Error Correlation Matrix where

35. 35 Bounds on Steady-State Mean Square Estimation Error Model Uncertainty and Unknown External Disturbances

36. SSRLS with Adaptive Memory (SSRLSWAM)

37. 37 The Cost Function cost function gradient of cost function row vector where SSRLS with Adaptive Memory (SSRLSWAM)

38. 38 Gradient of Cost Function SSRLS with Adaptive Memory (SSRLSWAM) Deterministic Gradient Define

39. 39 Gradient of Cost Function (‘contd) SSRLS with Adaptive Memory (SSRLSWAM)

40. 40 Tuning Forgetting Factor SSRLS with Adaptive Memory (SSRLSWAM) Stochastic Gradient Update using Stochastic Gradient Method

41. 41 SSRLSWAM – Complete Algorithm SSRLS with Adaptive Memory (SSRLSWAM)

42. 42 Initializing SSRLSWAM 2) Initialization using batch processing approach leads to delayed recursion -  offers better initialization SSRLS with Adaptive Memory (SSRLSWAM) Some suitable value < 1

43. Approximate Solution

44. 44 Approximate Solution using Symbolic Computations Approximate Solution Discrete Lyapunov Equation for S4RLS Can be computed Symbolically, Off-line

45. 45 Approximate Solution using Symbolic Computations (‘contd) Approximate Solution

46. 46 Approximate Solution using Symbolic Computations (‘contd) Approximate Solution Define Simplified Algorithm

47. 47 A Special Case (Constant Acceleration)

48. 48 A Special Case (Constant Acceleration) Approximate Solution System Matrices Symbolic Computation

49. 49 A Special Case (Constant Acceleration) – ‘Continued Approximate Solution Symbolic Computation

50. Computational Complexity

51. 51 Computational Complexities: Standard Algorithms

52. 52 Computational Complexities: SSRLSWAM and Variants

53. Example of Tracking a Noisy Chirp

54. 54 Chirp Signal Example of Tracking a Noisy Chirp Sinusoid whose frequency drifts with time Model Used by Tracker Model Mismatch Actual Model is Chirped Sinusoid Model

55. 55 Performance of SSRLSWAM Simulation Parameters Example of Tracking a Noisy Chirp

56. 56 Tuning of Forgetting Factor Simulation Parameters Example of Tracking a Noisy Chirp

57. 57 Performance of SSRLS Simulation Parameters Example of Tracking a Noisy Chirp

58. 58 Conclusion SSRLSWAM is a combination of SSRLS and stochastic gradient method S4RLSWAM alleviates computational burden of SSRLSWAM Suitable for time-varying scenario Compensates for model uncertainty to some extent Conclusion

59. 59 References Mohammad Bilal Malik, “State-space recursive least squares with adaptive memory”, Signal Processing Journal, Vol. 86, pp 1365-1374, 2006 References