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This presentation includes : - Introduction - Methodology - Data Fusion Techniques - ATC Applications - Current works
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Multi-sensor Data Fusion
Techno Briefing
Mr. Paveen Juntama
Air Traffic Service engineeringResearch & Development Department(RD.AS.)
Presented by
Contents
Overview
Methodology
Fusion Techniques
ATC Applications
Current works in RD.AS. (วว.สว.)
2
Overview
Overview
Methodology
Fusion Techniques
ATC Applications
Current works in RD.AS. (วว.สว.)
3
Problem-solving techniques based on the idea of integrating many answers into a single; the best answer
Process of combining data or information from various sensors to provide a robust and complete description of an process of interest
Multilevel process dealing with automatic detection, association, correlation, estimation and combinationof data or information from single or multiple sources
Definitions :
OverviewMultisensor Data Fusion (MDF)
4
Location and characterization of enemy units & weapons
Air to air / surface to air defense Battlefield intelligence Strategic warning etc.
Military applications :
OverviewMDF Applications
5
Central Monitoring systems (CMS) System Faults Detection Location & Identification Robotics & UAVs Medical etc.
Non military applications :
Improves accuracy
Improves precision
Improves availability
Reduces uncertainty
Supports effective decision making
MDF provides advantages over a single sensor :
OverviewWhy MDF ?
6
Methodology
Overview
Methodology
Fusion Techniques
ATC Applications
Current works in RD.AS. (วว.สว.)
7
MethodologyFusion Architectures
8
Measurement Fusion (Sensor data Fusion)
Feature-level Fusion
Decision-level Fusion (High-level data Fusion)
Data Fusion requires combining expertise in 2 areas :
Sensors
Information integration
Data fusion is essentially an information integration problem.
Data fusion can be categorized into 3 main classes based on the level of data abstraction used for fusion :
Direct fusion of data sensor
The sensors measuring the same physical phenomena are required.
Measurement Fusion (Sensor Data Fusion) :
MethodologyFusion Architectures
9
S1
Data LevelFusion
Association
S2
Sn
Feature
Extraction
Identity Declaration
Involves the extraction of representative features from sensor data
Features is combined into a single concatenated feature vector that is an input to a fusion node
Feature-level Fusion :
MethodologyFusion Architectures
10
S1
Association
S2
Sn
Feature
Extraction
Feature LevelFusion
+Identity
Declaration
Each sensor has made a preliminary determination of an entity’s location, attributes and identity before combining
Decision-level fusion algorithms are used such as weighted decision, Bayesian inference and Dempster-Shafer’s method
Decision-level Fusion :
MethodologyFusion Architectures
11
S1
S2
Sn
Identity Declaration
Feature
Extraction
Identity Declaration
Identity Declaration
Association
Declaration Level
Fusion
Fusion Techniques
Overview
Methodology
Fusion Techniques
ATC Applications
Current works in RD.AS. (วว.สว.)
12
Fusion Techniques
13
The available data fusion techniques can be classified into3 categories
Data Fusion
Data Association
Decision Fusion
State Estimation
The process of assign and compute the weight that relates the observations or tracks from one set to the observation of tracks of
another set.
Fusion TechniquesData Fusion Techniques
14
Data Association Techniques
Algorithms commonly used
Nearest Neighbors(NN), Probabilistic Data Association(PDA), Joint PDA(JPDA), Multiple Hypothesis Test (MHT) etc.
State estimation techniques aim to determine the state of the target under movement (typically the position) given the observation or
measurement.
Fusion TechniquesData Fusion Techniques
15
State Estimation (Tracking)
Algorithms commonly used
Maximum Likelihood (ML) & Maximum Posterior, Kalman Filter, Particle Filter, Covariance Consistency Methods etc.
Decision Fusion techniques aim to make a high-level inference about the events and activities produced from the detected targets.
Fusion TechniquesData Fusion Techniques
16
Decision Fusion
Algorithms commonly used
Bayesian Methods & Dempster-Shafer Inference, AbductiveReasoning, Semantic Methods etc.
𝑥1(𝑛)
𝑥2(𝑛)
𝑥𝑛(𝑛)
𝑥(𝑛)|
|
|
Fusion TechniquesData Fusion Techniques
17
Nearest Neighbors
Probabilistic Data Association
Joint PDA
Multiple Hypothesis Test
Maximum Likelihood
Kalman Filter*
Particle Filter
Covariance Consistency Methods
Bayesian Methods*
Dempster-Shafer Inference
Abductive Reasoning
Semantic Methods
*Bayesian approaches
Data Association State Estimation Decision Fusion
Fusion TechniquesBayesian Approaches
18
Bayes’ theorem
where the posterior probability, P(Y|X), represents the belief in the hypothesis Y given the information X. This probability is obtained by
multiplying the a priori probability of the hypothesis P(Y) by the probability of having X given that Y is true, P(X|Y)
Fusion TechniquesBayesian Approaches
19
A Recursive Bayesian Estimator : Kalman Filter
Address the general problem of trying to estimate the state of a discrete time process
Estimate a process using a recursive algorithm :– Prediction : estimate the process state at a certain time
– Correction : obtain feedback from noisy measurement
Fusion TechniquesBayesian Approaches
20
The need of Kalman Filter ?
System
MeasuringDevice
SystemError Sources
Control
UnknownSystem State
Measurement Error Sources
System state cannot be measures directly
Estimation “optimally” from measurements is required
Correction
PredictionPrediction
++
MeasurementModel
ProcessModel
Updated+
-
Error
Kalman Filter
𝑥(𝑛)
𝑥(𝑛)
Fusion TechniquesBayesian Approaches
21
Data Fusion with Kalman filter
MeasurementFusion
Track-to-trackFusion
Fusion TechniquesBayesian Approaches
22
Example results of Kalman filtering
ATC Applications
Overview
Methodology
Fusion Techniques
ATC Applications
Current works in RD.AS. (วว.สว.)
23
ATC ApplicationsSurveillance Data Processing
24
VHF GS
SAT GS
ATC CENTRE
ADS GS
MLAT/WAMMODE SSSRPSR
SAT NAVINMARSATSAT COM
Surveillance sensor environment
25
ATC ApplicationsSurveillance Data Processing
26
ATC ApplicationsSurveillance Data Processing
Selection techniques
Radar 1
Radar 2
|
|
|
Radar N
Multiple plots
switching
Selected Plots
plots
plots
plots
Radar 1
Radar 2
|
|
|
Radar N
Mono radar tracking
Mono radar tracking
Mono radar tracking
Multiple tracks
switching
plots
plots
tracks
tracks
Multiple plots switching method
Multiple tracks switching method
Selected Tracks
plots tracks
27
ATC ApplicationsSurveillance Data Processing
Average techniques
Multiple track average method
Multiple plot average method
Radar 1
Radar 2
|
|
|
Radar N
Mono radar tracking
Mono radar tracking
Mono radar tracking
Track-to-track
correlation
Track-to-track
fusion
Fused
Tracks
plots
plots
plots
tracks
tracks
tracks
Radar 1
Radar 2
|
|
|
Radar N
Plot-to-plot
correlation
Plot-to-plot fusion
Fused
Plots
plots
plots
plots
The technique consists in using all plots coming from any radar to update a unique synthetic common track
The track update is performed in the fly as soon as sensor report are received so that the reduction of the meantime update in multi-radar configuration improves the accuracy of the track parameter estimation.
These techniques contain more complex algorithms (Association + State Estimation + Decision Fusion)
Variable update techniques :
28
ATC ApplicationsSurveillance Data Processing
N
N-1
N-2
Correlation
Track Management
Track UpdateTrack
Initiation
N-kOutput
Tracks created
Tracks initiated
Non
associated
plots
Association
PairingNon
associated
tracks
Tracks to update
29
ATC ApplicationsSurveillance Data Processing
Comparison between techniques :
Selection & Average Techniques Variable Update Techniques
Low CPU load Medium to high CPU load
Low track accuracy Good track accuracy
Low track discrimination Good track discrimination
Manoeuvre detection in long time Manoeuvre detection in short time
Current works in RD.AS.
Overview
Methodology
Fusion Techniques
ATC Applications
Current works in RD.AS. (วว.สว.)
30
Current works in RD.AS.
31
System Architecture :
Multi Radar Tracking System (MRTS)
FusionSystem
SSR
SSR
SSR
ADS-B
Ground Station
Local Tracks
ADS-B Reports
System Tracks
32
Study of System tracks & ADS-B reports
Characteristics System tracks ADS-B reports
Update rates 500 ms 0.3-3 ms
Update rates / target 5 s 1 s
Data source MRTS GNSS
Identification Mode 3/A Callsign, Mode S
PerformanceHigh Availability
Low AccuracyHigh Accuracy
Low Availability
Current works in RD.AS.
Current works in RD.AS.
33
Study of System tracks & ADS-B reportsHorizontal Zoom
ADS-B reports lost in some periods (Low Availability) System tracks are less accurate in positioning compared to ADS-B
reports
Current works in RD.AS.
34
Fusion system
Sensor data Feature vector IdentityDeclaration
System tracks (CAT62),
ADS-B reports (CAT21)
Metadata Fused Tracks
Track Management, Track Initiation and Filtering are responsible for the association, correlation and state estimation techniques.
While Track-to-track Fusion corresponds to a Decision-level Fusion scheme.
Current works in RD.AS.
35
Results :
Remark :
There is only 1 ADS-B ground station in operation
Current works in RD.AS.
36
Problems & Difficulties
The difficult synchronization due to different time sources between ADS-B GS & MRTS
Difficulty of track correlation due to target identification problems
Current works in RD.AS.
37
Future works
Improve the synchronization mechanisms
Improve Fusion algorithms
Evaluate performance of the fusion system
Study possibilities for integrating data from new sensor types such as MLAT, WAM etc.
Study and characterize the system closing to the realistic environment as possible including a process model, a measurement model, Radar biases and ADS-B receiver biases.
Conclusion
38
Data fusion can be performed at 3 levels :
– Sensor data
– Feature vectors
– High level inferences
Several techniques has been developed to process data fusion at each level.
Fusion techniques can be used with one or more techniques; data association, state estimation or decision fusion, each technique contains various algorithms.
The use of fusion techniques and methodology depends on the environment of the system which include sensor characteristics, integrated information etc.