The Surprising Versatility of Statistical Pattern Recognition

1. The Surprising Versatility of Statistical Pattern Recognition Michael Turmon (JPL) Contributions from: Robert Granat (JPL), Han Park (Boeing) AISR Program Meeting University of Maryland Conference Center 5 October 2006

2. Outline Object recognition, tracking, track analysis Mixture modeling/unsupervised clustering Time series analysis Supported by an earlier AISR award Today, a high-speed recap only Time series search in ISS sensor archives …using time series analysis techniques Behavior classification in ISS sensor streams …using discriminants built on mixture models

3. Object Identification and Analysis Identification: Find objects in multispectral science images Featuring Gaussian mixture models Tracking: Link identified objects in series of images Trajectory Analysis: Model and classify object tracks Featuring Hidden Markov Models

4. Identification: Integrating Multimode Imagery Can not distinguish classes from just one observable Select mixture model using sample images labeled by scientists One mixture model per class To classify, compute each class’s probability under its mixture and take the largest Move beyond ad hoc threshold rules to allow arbitrary class separators

5. Identification: Results Turmon et al., “Statistical Pattern Recognition for Labeling Solar Active Regions: Application to SoHO/MDI Imagery,” Astrophysical Journal, March 20 2002, 396-407.

6. Feature Identification: Infusion This software will be used in the HMI data pipeline at Stanford HMI imager will fly in 2008 on board SDO, the first LWS mission http://sdo.gsfc.nasa.gov and http://hmi.stanford.edu HMI’s data volume is unprecedented in Solar Physics 4096x4096 pixel images every 90 seconds These data volumes make it more important to focus attention HMI/SDO is the successor to MDI/SoHO, on which these results are based This software is being evaluated to classify the Kitt Peak imagery Funded by NASA Sun Earth Connection, with Harry Jones, Kitt Peak Five observables (field, intensity + equivalent width, line depth, velocity) Taken 1992-2003, replaced by an upgraded vector spectromagnetograph Perhaps the best earth-based synoptic magnetic imagery Significant training data already gathered by Karen Harvey This software has been baselined by CNES Picard Picard has been given the go-ahead by CNES for 2009 launch http://smsc.cnes.fr/PICARD/ Picard will measure tiny variations in solar diameter and shape Active region recognition and rejection is important to its delicate results Software must clear ITAR hurdles

7. Object Tracking Methods Associate objects in beforeand after images Correlation-based tracker Motion model: deterministic drift plus stochastic uncertainty For sunspots or cyclones, have motion and correlation on the sphere Correlation measure between a in A and b in B is D(a,b) Solve assignment problem to match A up to B: with P a permutation matrix Solution by linear programming For our applications, key is to get deterministic drift correct

8. Object Tracking: Sunspots

9. Object Tracking: Ocean Eddies Eddies in shallow-water ocean simulation (Toshio M. Chin, JPL) State (position, size) of two labeled eddies through time, lower left Two subclasses of eddies are apparent, lower right We developed Kalman/HMM tools to analyze these track motions

10. …putting on the black hat…

11. Time Series Search: Objective and Overview A flight controller sees an interesting behavior, and wants to find similar examples in past data Exploratory data analysis Identifying precursors of conditions to be avoided Prelude to training special-purpose detectors for the behavior How search works The user selects a time series snippet that contains the desired behavior A statistical model is made for the snippet This model is used to search in archived data for similar behaviors Search, meet Beta Gimbal Array (BGA) BGA is prone to motor stalls and consequent circuit breaker trips EVA required to reset circuit breaker Flight controllers want to identify motor stalls in advance but have no automatic way to find them in historic data for better modeling BGA motor current shows potential precursors to motor stalls

12. Search: Operational Flow

13. Search: Modeling a Snippet In principle, any state-space statistical model could provide a basis for comparison of the snippet to the historical record In this work we used hidden Markov models (HMMs) These models separate the time series into discrete activity types Provides some robustness to noise and drift Our model fitting method (RDAHMM) ensures robust fitting Regularized Deterministic-Annealing HMM parameter estimation Produces good solutions on the first try Easy extension to multivariate sensor search PhD work of Robert Granat, 2001 The internals of the fitting procedure are invisible to the user

14. Search: Match Results

15. Time Series Behavior Classification Model-based time series classification is an automatic way to focus attention on the most interesting parts of the signal Analysis can learn HMM models for specific signatures without training or significant expert involvement (e.g., on right, or LF signature on left) Alternatively, identify outliers or novel behaviors (e.g., transient on right) These results are due to Robert Granat, JPL

16. Identifying Behaviors in Sensor Time Series ISS Control Moment Gyro = CMG Maintains attitude of International Space Station (ISS) Four on ISS; two failed and were replaced Two Gimbals and Motors (outer/inner) and Gyro Motor

17. SSRMS – CMG Correlation SSRMS activity typically causes CMG disturbances False alarms could result from such “anomalous” CMG disturbances Motivates need for behavior classification

18. Dynamical Invariant Anomaly Detector Fits autoregressive (AR) model to sensor stream Recover a’s by fitting this model: Changes in AR coefficients from nominal model indicate underlying system has changed Quantify deviations from nominal Anomaly is declared when threshold is passed Detector idea due to Zak, Fijany, Park, et al.

19. ISS Activity Detection in Action Example data from CMG3, about two weeks shown Question: Can this classification be done automatically by clustering?

20. ISS Behavior Classification Fit a Gaussian mixture to the DIAD autoregression parameters Four behaviors separate well (left), corresponding to objective conditions (right) Improvements Classification, not just 0/1 anomaly detection Eliminates hand selection of models (finding a nominal model) Objective determination of class boundaries versus intuitive detector criterion “Delta” Quantitative outlier detection is possible using the mixture