© Saab AB 2007

Multicore Applications at Data Fusion - Saab SDS

Dr. Mats Ekman

© 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

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++

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?

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

03-10-06

SAAB SYSTEMS

Ongoing and Future Multicore Applicationsat Saab – CoderMP cooperation

• Particle filtering

• Anomaly detection

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…

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

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

IDEALSIntegrated Detection and Estimation ALgorithms Solutions for data processing and fusion

Nederland

IDEALS-08:0084 1.0

10

Particle filters

Resampling

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

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

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)

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

© 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