2. Brief Introduction to the Alberta Group of MITACS-PINTS Center I. Groups: (Project Leader : Prof. Mike Kouritzin) University of Alberta (base), H.E.C. (led by Prof. Bruno Remillard), University of Waterloo (led by Prof. Andrew Heunis). II. Sponsors and their interests (1) Lockheed Martin Naval Electronics and Surveillance System: Surveillance and tracking, search and rescue, anti-narcotic smuggling, air traffic management, and global positioning.

3. II. Sponsors and their interests (cont.) (2) Lockheed Martin Canada, Montreal: same interests as above. (3) Acoustic Positioning Research Inc., Edmonton: Track stage performers using acoustic techniques and adjust lighting, sound effects. Create performer-movement- controlled product. (4) VisionSmart, Edmonton: Quality control of industrial processes such as real time analysis of oriented strand board (OSB) density variations using thermography techniques and pattern recognition of naturally occurring substances etc. (5) Stantec (future) : environmental monitoring, pollution tracking.

4. III. Postdoctoral Fellow and Graduate Students Dr. Hongwei Long (MITACS Industrial PDF) Dr. Wei Sun (PIms-MITACS Industrial PDF) David Ballantyne (Graduate Student) Calvin Chan (Graduate Student) Christina Popescu (Graduate Student) Cody Hyndman (Graduate Student, Waterloo) Nabel Saimi (Graduate Student, Trois Rivieres) Abderhamane Ait-Simmou (Graduate Student, Trois Rivieres) Paul Wiebe (Graduate Student) Xinjian Ma (Undergraduate Student) Surrey Kim (Undergraduate Student)

5. IV. Sample from Simulation Front (1) Particle filters for combined state and parameter estimation: application to tracking of a dinghy lost at sea. (By Hubert Chan and Michael Kouritzin). (2) Branching particle nonlinear filtering: application to highly directed signal dynamics, highly definitive observation or ``clipped’’ observation data. (By David Ballantyne, John Hoffman and Michael Kouritzin). (3) Convolutional filters: application to financial engineering and tracking by combining signal prediction and parameter estimation. (By Michael Kouritzin, Bruno Remillard and Calvin P. Chan). (4) Interacting particle filters: application to tracking of multiple targets. (By David Ballantyne, Hubert Chan and Michael Kouritzin).

6. Simulation Front Cont. ( 5) Markov chain approximations: application to a filtering model for reflecting diffusions (e.g. fish in a tank). (By David Ballantyne, Michael Kouritzin, Hongwei Long and Wei Sun). (6) Branching particle filtering: application to acoustic positioning system in theater. (By Michael Kouritzin, Surrey Kim and Victor Ma)

7. (V) Sample from Theoretical Front (1) Markov chain approximation to nonlinear filtering equations for reflecting diffusion processes. (By Michael Kouritzin, Hongwei Long and Wei Sun). (2) On the generality of the classical filtering equations. (By Michael Kouritzin and Hongwei Long). (3) Empirical processes based on pseudo-observations: the multivariate case. (By K. Ghoudi and Bruno Remillard) (4) Nonparameter weighted-symmetry tests. (By B. Abdous, K. Ghoudi and Bruno Remillard). (5) Testing for randomness against serial dependence. (By C. Genest, J.F. Quessy and B. Remillard).

8. Theoretical Front (continued) (6) Functional central limit theorems for interacting particle systems. (By Pierre Del Moral and Michael Kouritzin). (7) Explicit solutions for vector Ito’s equations. (By Michael Kouritzin and Bruno Remillard). (8) On Uniqueness of Solutions for the Stochastic Differnectial Equations of Nonlinear Filtering. (By Vladimir Lucic and Andrew Heunis)

9. (VI.) Ideas for the future (1) Implement chaos method and try out with random environments. (2) Filtering when observations in random environments (3) Tracking and estimation of bacteria and other species.

10. Overview 1.Surrey Kim - Overview and Review of Filters and Filtering Procedure. (8-10 mins). 2.Hong Wei Long. - Markov Chain Approximation to Non-Linear Filters. (13-15 mins). 3.Q & A.

11. Signal Simulation A vessel with at sea transporting narcotics or a dinghy lost at sea.

12. Can You Find The Signal ? (Mild Observation Noise)

13. Can You Find The Signal ? (Medium Observation Noise)

14. Can You Find The Signal ? (Realistic Observation Noise)

15. Filtering Procedure 1. Modeling an unobserved signal. 2. Modeling the partial/noisy/distorted observation. 3. Filter outputs a conditional distribution estimate of the signal’s past/present/future state, based on observations. += Signal & Observation Model Noisy ObservationsFilter’s Estimate

16. Mathematical Formulation of Filtering To do filtering we require a predictive model for (signal observations). The most classical predictive model is: a) Signal is a measurable Markov process b) The observation are : where is a measurable function and B is a Brownian motion independent of. Is there a stochastic evolution equation for Yes !

17. Kushner (1967) guessed right. Fujisaki, Kallianpur, and Kunita (1972) proved it rigorously under Kurtz and Ocone (1988) wondered if this condition could be weakened. Kouritzin and Long (2001) proved that if then where is the innovation process. is the weak generator for. The new condition is much more general, allowing for example and with - stable distributions with. (old) (new)

18. Filter Simulation 1 Single Target Tracking: Dinghy Lost At Sea Signal Noisy Observation Branching Particle Filter’s estimate

19. Filter Simulation 2 Multiple Target Tracking: 3 Dinghies

20. Filter Applications 1. Tracking: current real-time state of a signal. 2. Smoothing: past state of a signal. 3. Prediction: future state of a signal. 4. Historical Path: includes all estimates from 1, 2, 3.

21. Filter Simulation 3 Historical Filter: Dinghy Lost At Sea Green – past path & estimate of the confidence. White - current state & estimate of the confidence. Magenta – future path & estimate of the confidence.

22. Filter Simulation 4 (Stage Performer (Acoustic Positioning Research)

23. Markov Chain Approximations to Nonlinear Filters

24. The Fish Farming Problem Unknown number of fish Swimming randomly (assumption: fish don’t have dinner plans). Very distorted and noisy pictures. Very dirty water which add to observation corruption (fish eat => fish s!*#). Blind spots due to hiding places & other fishes.

25. I. Formulation of Filtering Problem Signal: reflecting diffusions in rectangular region D (e.g. fish in a tank). Mathematical model described by Skorohod SDE:

26. Formulation of Filtering (cont.) The associated diffusion generator Observation: distorted, corrupted, partial: Optimal filter: to evaluate

27. Formulation of Filtering (cont.) Reference probability measure: Under : and are independent, is a standard Brownian motion. Kallianpur-Striebel formula (Bayes formula):

28. Formulation of Filtering (cont.) has a density under our assumptions, which solves the Zakai equation: Kushner-Huang’s wide-band observation noise approximation converges to in distribution

29. Formulation of Filtering (cont.) Find numerical solutions to the above random PDE by replacing with, Kushner or Bhatt-Karandikar’s robustness result can handle this part: the approximate filter converges to optimal filter.

30. II. Construction of Markov Chains Use stochastic particle method developed by Kurtz, Arnold, Kotelenez, Blount, Kouritzin and Long. We have the following Dirichlet form : Divide the region D into cells and Construct discretized operator via (discretized) Dirichlet form.

31. Construction of Markov Chains (cont.) Dirichlet form: a coercive closed bilinear form associated with the diffusion generator. (Signal observation) defined on a probability space Use another probability space to construct independent Poisson processes and a sequence of Bernoulli trials. Define a product probability space

32. Construction of Markov Chains (cont.) : number of particle in cell k at time t

33. Markov Chain Particle Based Filter Simulation

34. Construction of Markov Chains (cont.) { } is modelled as an inhomogeneous Markov chain via random time changes. Use much slower transition rates which makes the implementation much more efficient. Particles evolve in each cell according to birth and death from reaction (involving observation data), random walks from diffusion and drift.

35. Construction of Markov Chains (cont.) denotes mass of each particle The approximate Markov process is given by From, we can construct a unique probability measure defined on the cadlag path space for each. Using martingale theory and Dirichlet form theory to analyze the mathematical structure of our Markov chains

36. III. Laws of Large Numbers We have both the quenched and annealed laws of large numbers : Quenched approach: fixing the sample path of observation process Annealed approach: considering the observation process as a random medium for Markov chains

37. IV. Concluding Remarks Find implementable approximate solutions to filtering equations. Our method differs from previous ones such as Monte Carlo method (using Markov chains to approximate signals, Kushner 1977), interacting particle method (Del Moral, 1997), weighted particle method (Kurtz and Xiong, 1999, analyze), and branching particle method (Kouritzin, 2000) Our algorithm is far more efficient for the class of reflecting signals considered in this work.