Multisensor Data Fusion : Techno Briefing


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This presentation includes :
- Introduction
- Methodology
- Data Fusion Techniques
- ATC Applications
- Current works

Published in: Data & Analytics, Technology

Multisensor Data Fusion : Techno Briefing

  1. 1. Multi-sensor Data Fusion Techno Briefing Mr. Paveen Juntama Air Traffic Service engineering Research & Development Department (RD.AS.) Presented by
  2. 2. Contents  Overview  Methodology  Fusion Techniques  ATC Applications  Current works in RD.AS. (วว.สว.) 2
  3. 3. Overview  Overview  Methodology  Fusion Techniques  ATC Applications  Current works in RD.AS. (วว.สว.) 3
  4. 4.  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 combination of data or information from single or multiple sources Definitions : Overview Multisensor Data Fusion (MDF) 4
  5. 5.  Location and characterization of enemy units & weapons  Air to air / surface to air defense  Battlefield intelligence  Strategic warning etc. Military applications : Overview MDF Applications 5  Central Monitoring systems (CMS)  System Faults Detection  Location & Identification  Robotics & UAVs  Medical etc. Non military applications :
  6. 6.  Improves accuracy  Improves precision  Improves availability  Reduces uncertainty  Supports effective decision making MDF provides advantages over a single sensor : Overview Why MDF ? 6
  7. 7. Methodology  Overview  Methodology  Fusion Techniques  ATC Applications  Current works in RD.AS. (วว.สว.) 7
  8. 8. Methodology Fusion 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 :
  9. 9.  Direct fusion of data sensor  The sensors measuring the same physical phenomena are required. Measurement Fusion (Sensor Data Fusion) : Methodology Fusion Architectures 9 S1 Data Level Fusion Association S2 Sn Feature Extraction Identity Declaration
  10. 10.  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 : Methodology Fusion Architectures 10 S1 Association S2 Sn Feature Extraction Feature Level Fusion + Identity Declaration
  11. 11.  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 : Methodology Fusion Architectures 11 S1 S2 Sn Identity Declaration Feature Extraction Identity Declaration Identity Declaration Association Declaration Level Fusion
  12. 12. Fusion Techniques  Overview  Methodology  Fusion Techniques  ATC Applications  Current works in RD.AS. (วว.สว.) 12
  13. 13. Fusion Techniques 13 The available data fusion techniques can be classified into 3 categories Data Fusion Data Association Decision Fusion State Estimation
  14. 14. 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 Techniques Data Fusion Techniques 14 Data Association Techniques Algorithms commonly used Nearest Neighbors(NN), Probabilistic Data Association(PDA), Joint PDA(JPDA), Multiple Hypothesis Test (MHT) etc.
  15. 15. State estimation techniques aim to determine the state of the target under movement (typically the position) given the observation or measurement. Fusion Techniques Data Fusion Techniques 15 State Estimation (Tracking) Algorithms commonly used Maximum Likelihood (ML) & Maximum Posterior, Kalman Filter, Particle Filter, Covariance Consistency Methods etc.
  16. 16. Decision Fusion techniques aim to make a high-level inference about the events and activities produced from the detected targets. Fusion Techniques Data Fusion Techniques 16 Decision Fusion Algorithms commonly used Bayesian Methods & Dempster-Shafer Inference, Abductive Reasoning, Semantic Methods etc. 𝑥1(𝑛) 𝑥2(𝑛) 𝑥 𝑛(𝑛) 𝑥(𝑛) | | |
  17. 17. Fusion Techniques Data 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
  18. 18. Fusion Techniques Bayesian 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)
  19. 19. Fusion Techniques Bayesian 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
  20. 20. Fusion Techniques Bayesian Approaches 20 The need of Kalman Filter ? System Measuring Device System Error Sources Control Unknown System State Measurement Error Sources  System state cannot be measures directly  Estimation “optimally” from measurements is required Correction PredictionPrediction ++ Measurement Model Process Model Updated+ - Error Kalman Filter 𝑥(𝑛) 𝑥(𝑛)
  21. 21. Fusion Techniques Bayesian Approaches 21 Data Fusion with Kalman filter Measurement Fusion Track-to-track Fusion
  22. 22. Fusion Techniques Bayesian Approaches 22 Example results of Kalman filtering
  23. 23. ATC Applications  Overview  Methodology  Fusion Techniques  ATC Applications  Current works in RD.AS. (วว.สว.) 23
  24. 24. ATC Applications Surveillance Data Processing 24 VHF GS SAT GS ATC CENTRE ADS GS MLAT/WAMMODE SSSRPSR SAT NAVINMARSATSAT COM Surveillance sensor environment
  25. 25. 25 ATC Applications Surveillance Data Processing
  26. 26. 26 ATC Applications Surveillance 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. 27. 27 ATC Applications Surveillance 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
  28. 28.  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 Applications Surveillance Data Processing N N-1 N-2 Correlation Track Management Track Update Track Initiation N-k Output Tracks created Tracks initiated Non associated plots Association Pairing Non associated tracks Tracks to update
  29. 29. 29 ATC Applications Surveillance 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
  30. 30. Current works in RD.AS.  Overview  Methodology  Fusion Techniques  ATC Applications  Current works in RD.AS. (วว.สว.) 30
  31. 31. Current works in RD.AS. 31 System Architecture : Multi Radar Tracking System (MRTS) Fusion System SSR SSR SSR ADS-B Ground Station Local Tracks ADS-B Reports System Tracks
  32. 32. 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 Performance High Availability Low Accuracy High Accuracy Low Availability Current works in RD.AS.
  33. 33. Current works in RD.AS. 33 Study of System tracks & ADS-B reports Horizontal Zoom  ADS-B reports lost in some periods (Low Availability)  System tracks are less accurate in positioning compared to ADS-B reports
  34. 34. Current works in RD.AS. 34 Fusion system Sensor data Feature vector Identity Declaration 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.
  35. 35. Current works in RD.AS. 35 Results : Remark : There is only 1 ADS-B ground station in operation
  36. 36. 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
  37. 37. 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.
  38. 38. 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.