1. © Saab AB 2007 Multicore Applications at Data Fusion - Saab SDS Dr. Mats Ekman

2. © Saab AB 2007 Saab Data Fusion Group A core team of about 18 engineers, including 6 PhDs Active since 1984 Air, Land, Naval, Civil domains Research & Development Marketing/Sales support Technical tender support Analysis/Design Implementation Testing, customer training Multi Sensor Tracker (MST) Parameter tuning Algorithm Redesign Alterations, tests x t+1 =f(x t )+w t y t+1 =h(x t )+e t

3. 03-10-06 SAAB SYSTEMS plots tracks sensor Multicore Implementation Example 1- a success 2 step process: - get the positions - calculate scalar products and compare with the plane Since objects are independent  parallelization of the process TBB library (Intel Threading Building block) for C++

4. 03-10-06 SAAB SYSTEMS Results Total process load Tested on a 4 cores  local process 2.5 times faster. Delivered to customer - core 2. Drawback: need to modify the code – cannot use iterators. Some overhead using threading, cache misses?

5. 03-10-06 SAAB SYSTEMS Example 2 – a failure plots tracks sensor Association Process: pre-processing – transformation to polar coordinates and clustering Data association – work on each cluster, since cluster are independent  parallelization Technical problem: 1.Static variables – several treads working on the same variables 2. Common resources – ex. Id for tracks are obtained from a common track bank  several treads in trying to access the bank  lock (mute, sync) Solution: restructure the code Id bank void set Void put

6. 03-10-06 SAAB SYSTEMS Ongoing and Future Multicore Applications at Saab – CoderMP cooperation Particle filtering Anomaly detection

7. Nederland IDEALS-08:0084 1.0 7 IDEALS Integrated Detection and Estimation ALgorithms Solutions for data processing and fusion Intro to particle filtering A target here and now… …expected to arrive here… …but radar plot appeared here… …so the target is probably here prediction – updating – prediction – updating…

8. Nederland IDEALS-08:0084 1.0 8 IDEALS Integrated Detection and Estimation ALgorithms Solutions for data processing and fusion Probability densities A target here and now… …expected to arrive here… …but radar plot appeared here… …so the target is probably here

9. Nederland IDEALS-08:0084 1.0 9 IDEALS Integrated Detection and Estimation ALgorithms Solutions for data processing and fusion Filtering principles Exactly: Impractical Ellipses/gaussian distributions: Kalman filtering Particle filters

10. Nederland IDEALS-08:0084 1.0 10 IDEALS Integrated Detection and Estimation ALgorithms Solutions for data processing and fusion Particle filters Resampling

11. Nederland IDEALS-08:0084 1.0 11 IDEALS Integrated Detection and Estimation ALgorithms Solutions for data processing and fusion Comparison (1) Standard KalmanConstrained KalmanParticle filter

12. Nederland IDEALS-08:0084 1.0 12 IDEALS Integrated Detection and Estimation ALgorithms Solutions for data processing and fusion Comparison (2) Particle filters - superior at severe nonlinearities Standard KalmanConstrained KalmanParticle filter

13. Parallelization of PFs

14. Videos Real Data from Enköping Acoustic Sensors No road constraints Simulated Data Acoustic Sensors Comparison between different road constrained filters Mix of real data from Gotland and simulated data Radar, acoustic and seismic sensors Road constraints Simulated Data Terrain constraints Comparinson with only road constraints

15. © Saab AB 2007 Anomaly detection – complement to Rule Based Situation Assessment  Identify targets that do not behave like the majority  Here: Vessels south of Sweden.  Blue: Training data  Green: Test data identified as normal  Red: Test data identified as abnormal