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Multicore Applications at Data Fusion - Saab SDS

Explore the applications of multicore technology in data fusion at Saab, including algorithm redesign, parameter tuning, and implementation for improved performance. Experience faster processing, parallelization, and superior filtering techniques with particle filters and anomaly detection.

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Multicore Applications at Data Fusion - Saab SDS

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

  2. 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 xt+1=f(xt)+wt yt+1=h(xt)+et Parameter tuning Algorithm Redesign Alterations, tests Multi Sensor Tracker (MST)

  3. 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++ Multicore ImplementationExample 1- a success plots tracks sensor

  4. Results • 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? Total process load

  5. Example 2 – a failure • Association Process: • pre-processing – transformation to polar • coordinates and clustering • Data association – work on each cluster, since cluster are independent parallelization plots tracks • Technical problem: • 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 sensor

  6. Ongoing and Future Multicore Applications at Saab – CoderMP cooperation • Particle filtering • Anomaly detection

  7. …expected to arrive here… …but radar plotappeared here… …so the target is probably here Intro to particle filtering A target here and now… prediction – updating – prediction – updating…

  8. …expected to arrive here… …but radar plotappeared here… …so the target is probably here Probability densities A target here and now…

  9. Filtering principles Exactly: Impractical Ellipses/gaussian distributions: Kalman filtering Particle filters

  10. Resampling Particle filters

  11. Comparison (1) Standard Kalman Constrained Kalman Particle filter

  12. Comparison (2)Particle filters - superior at severe nonlinearities Standard Kalman Constrained Kalman Particle 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. 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

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