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© Saab AB 2007
Multicore Applications at Data Fusion - Saab SDS
Dr. Mats Ekman
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© 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 tuningAlgorithm RedesignAlterations, tests
xt+1=f(xt)+wt
yt+1=h(xt)+et
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03-10-06
SAAB SYSTEMS
plots
tracks
sensor
Multicore ImplementationExample 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++
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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?
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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 workingon 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
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03-10-06
SAAB SYSTEMS
Ongoing and Future Multicore Applicationsat Saab – CoderMP cooperation
• Particle filtering
• Anomaly detection
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IDEALSIntegrated Detection and Estimation ALgorithms Solutions for data processing and fusion
Nederland
IDEALS-08:0084 1.0
7
Intro to particle filtering
A target here and now…
…expected to arrive here…
…but radar plotappeared here… …so the target is probably here
prediction – updating – prediction – updating…
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IDEALSIntegrated Detection and Estimation ALgorithms Solutions for data processing and fusion
Nederland
IDEALS-08:0084 1.0
8
Probability densities
A target here and now…
…expected to arrive here…
…but radar plotappeared here…
…so the target is probably here
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IDEALSIntegrated Detection and Estimation ALgorithms Solutions for data processing and fusion
Nederland
IDEALS-08:0084 1.0
9
Filtering principles
Exactly: Impractical
Ellipses/gaussian distributions: Kalman filtering
Particle filters
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IDEALSIntegrated Detection and Estimation ALgorithms Solutions for data processing and fusion
Nederland
IDEALS-08:0084 1.0
10
Particle filters
Resampling
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IDEALSIntegrated Detection and Estimation ALgorithms Solutions for data processing and fusion
Nederland
IDEALS-08:0084 1.0
11
Comparison (1)
Standard Kalman Constrained Kalman Particle filter
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IDEALSIntegrated Detection and Estimation ALgorithms Solutions for data processing and fusion
Nederland
IDEALS-08:0084 1.0
12
Comparison (2)Particle filters - superior at severe nonlinearities
Standard Kalman Constrained Kalman Particle filter
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Parallelization of PFs
Initialisation
Resampling
Normalise weights
Prediction
Update
Particle batch 1 Particle batch 2 Particle batch i Particle batch K
Initialisation
Resampling
Normalise weights
Prediction
Update
Initialisation
Resampling
Normalise weights
Prediction
Update
Initialisation
Resampling
Normalise weights
Prediction
Update
Redistribute particles between batches (i.e. communication)
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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
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© 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