Area Access SurveillanceTechnologiesS. Fioravanti, A. TeseiNovember 21, 2012
Outline•    CMRE history and mission•    Underwater Monitoring Technologies•    Hardware design and development•    Detect...
•  world-class NATO scientific   research and experimentation   facility, La Spezia, Italy   –  ocean science, modeling   ...
1989 End of cold war: many examples of dual-use military technologies→  Design and implementation of a calibration facilit...
Underwater MonitoringTechnologies       Autonomous sensing  Acousticacoustic     Passive Monitoring                       ...
Acous&c:	  Triangula&on	  among	  distributed	  sensors	     EM waves                       Shore LabCell-phoneGPSMajor di...
Examples of noise from vessels            Mid-speed, small ship. Spectrogram (dB re. 1 µPa)                            NUR...
Slant PlaneExploitation of time coherence                                   Hyd 1of signal received by eachsensor pair of ...
System design•  Each Station   –  Sparse Tetrahedral Array of 
      Four low-noise, preamp hydrophones 
      (100 kHz ba...
Deployments•  2011-2012: La Spezia harbor  –  Test of performances and     assessment of performances     degradation•  Ma...
Localization from one station and fromtwo stations                                                      P                 ...
ARGOMARINE Sea Trials 2012
 (NURC & Elba Island)•  Acoustic characterization of the test sites 
   (ambient noise)•  Ocean...
Enfola (Elba Island) test site                    Tripods deployment:                    •  40m depth                    •...
Experimental Results                       Fused Track in Geographic Coordinates                             GPS ground-tr...
Experimental Results                  Performances:                  40 m max error over                  700m range (6 %)...
Classification
RVM (Relevance Vector Machine)classifier•  Supervised statistical method (19 features   selected)•  Binary•  ...
Feature extraction from data                                                PSD function                                  ...
Classification results               10                                                                                    ...
Autonomous Sensing Vehicles•  eFolaga with e-nose
Complete MIS Integration                 AUV positions                       &                  E-nose data               ...
Integration with ARGOMARINE MIS
Acoustic Detection, Localization and Classification•  Concept successfully validated•  Advanced prototype system•  Possible...
Test at sea•  May 2012: eNose mission integrated into   ARGOMARINE MIS•  Sep. 2012: acquisition of sampling oil   signals•...
OBJECTIVE: Find the optimum samplingdesigns for an AUV-mooring ocean observingnetwork•    METHODOLOGY: The problem is deco...
AUV mission plannerDefinition of Operational Constraints     • Area     • Time constraints     • Vehicle speed     • Number...
Experimental Design forARGOMARINE               Find an optimum                          mission                          ...
AUV mission -Result for ARGOMARINE                                Optimum                                trajectory       ...
WP4.5 Integration•  Current vehicle capabilities•  Macro Tasks    –    Surface navigation    –    Gliding mission    –    ...
MOOS-IvP•  MOOS: Mission Oriented Operating Suite•  IvP: Interval Programming a mathematical programming model   for multi...
Moos-IVP integration:        E-Folaga Main         controller        (front-seat driver)                                  ...
Many thanks to CMRE team•  Alberto A. •  Alberto G.•  Alessandra T.•  Federico C.•  Lavinio G.•  Piero G.•  Vittorio G.Ale...
Conclusions•  Mission accomplished
Preamp.                                                               toHyd.                                              ...
ARGOMARINE Final Conference - CMRE-NATO - Stefano Fioravanti, Alessandra Tesei
ARGOMARINE Final Conference - CMRE-NATO - Stefano Fioravanti, Alessandra Tesei
ARGOMARINE Final Conference - CMRE-NATO - Stefano Fioravanti, Alessandra Tesei
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ARGOMARINE Final Conference - CMRE-NATO - Stefano Fioravanti, Alessandra Tesei

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A presentation about the Area Access Surveillance Technologies in the field of ARGOMARINE Project

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ARGOMARINE Final Conference - CMRE-NATO - Stefano Fioravanti, Alessandra Tesei

  1. 1. Area Access SurveillanceTechnologiesS. Fioravanti, A. TeseiNovember 21, 2012
  2. 2. Outline•  CMRE history and mission•  Underwater Monitoring Technologies•  Hardware design and development•  Detection and Localization•  Classification •  Experimental results•  Conclusions
  3. 3. •  world-class NATO scientific research and experimentation facility, La Spezia, Italy –  ocean science, modeling and simulation, acoustics and other disciplines•  over 50 years of service •  The Centre disposes of an unique research structure in the European panorama –  employs scientists from all NATO countries –  two research platforms for experiments at sea –  development systems and laboratories for acoustic and oceanographic studies –  facilities for various instrument calibration –  electronic and mechanical design laboratories –  autonomous underwater and surface vehicles
  4. 4. 1989 End of cold war: many examples of dual-use military technologies→  Design and implementation of a calibration facility for oceanographic instrumentation which provides assistance to nearly all the Italian marine research institutions and to other countries in southern Europe→  Design and implementation of advanced environmental monitoring systems→  CMRE starts studies on the effect of anthropogenic noise on marine animals –  main purpose is to draw a mitigation protocol on the influence on animals made by artificial acoustic emissions used for military or geological applications –  joined several national and international research institution expert on this topic –  from 1999, carried out many big experimental campaigns at sea with the participation of research institutions from all over the world . . . . .→ ARGOMARINE: area access monitoring technologies
  5. 5. Underwater MonitoringTechnologies Autonomous sensing Acousticacoustic Passive Monitoring UW Modem Passive acoustic
  6. 6. Acous&c:  Triangula&on  among  distributed  sensors   EM waves Shore LabCell-phoneGPSMajor differences: Acoustic•  Complex environment waves •  Noise & Sound propagation•  Variety of unknown soundsources (blind monitoring) 3D VIEW
  7. 7. Examples of noise from vessels Mid-speed, small ship. Spectrogram (dB re. 1 µPa) NURC rubber boat. Spectrogram (dB re. 1 µPa) 30 110 30 110 Slow, mid-size, leisure boat. Spectrogram (dB re. 1 µPa) 30 110 High level @ LF High level in Wide Band High level @ LF & MF Limited prop. cavitation 100 25 Strong prop. cavitation 100 No cavitation 100 Few spectral lines Several spectral lines Many spectral lines 25 25 90 90 90 20 20 20 Frequency (kHz)Frequency (kHz) Frequency (kHz) 80 80 80 15 15 15 70 70 70 10 10 10 60 60 60 5 5 5 50 50 50 40 40 40 0 1 2 3 4 5 6 7 8 5 6 7 8 9 10 11 12 13 0 1 2 3 4 5 6 7 8 Time (sec) Time (sec) Time (sec)
  8. 8. Slant PlaneExploitation of time coherence Hyd 1of signal received by eachsensor pair of each array Hyd 2 ΔTRequirements:•  Sparse hydrophones (d>>λ)•  High sampling frequency X-Correlogram EF2 Tripod 1 - Pair 23 0 τ α 50 Hyd 2 d Hyd 1 Hydrophone PairBearing (deg) 100 τ: Time Delay α : Bearing angle 150 α  = acos ( τ cw / d ) 0 10 20 30 40 50 Time (sec)
  9. 9. System design•  Each Station –  Sparse Tetrahedral Array of 
 Four low-noise, preamp hydrophones 
 (100 kHz bandwidth) –  SCU & Digitalizer (192 kHz SF) –  Pan-Tilt-Compass-Depth sensor (serial data integrated into digital data flow to shore)•  Fiber-optic-cable connection to shore•  Simultaneous acquisition of continuous data flow from both stations on shore –  Real-time Reception, Acquisition, Display & Processing of data from both stations –  Integration in the same data files of acoustic, orientation, and other possible serial data
  10. 10. Deployments•  2011-2012: La Spezia harbor –  Test of performances and assessment of performances degradation•  May 2012: 3 weeks in Elba Island at sea recording data with and without ground truth tracks –  collected several Terabyte of data to be used for algorithm assessment and validation and for classifier training set
  11. 11. Localization from one station and fromtwo stations P Water DepthOne Tripod k z y Eleva.on   Top View Azimuth   Two Tripods: x 2D Triangulation θ2 Tripod1 θ1 Tripod2
  12. 12. ARGOMARINE Sea Trials 2012
 (NURC & Elba Island)•  Acoustic characterization of the test sites 
 (ambient noise)•  Oceanographic data on the field (SVP)•  Acoustic data collection under controlled conditions: –  Simultaneous data acquisition from BOTH 
 uw stations during the run of an inflatable boat equippedGPS   with GPS antenna antenna   –  Integration of GPS ground-truth position 
 data into acoustic data files•  Blind acoustic monitoring
  13. 13. Enfola (Elba Island) test site Tripods deployment: •  40m depth •  About 450-550m off shore •  Sandy-posidonia seabed •  Relatively quiet environment evidence of No thermoclyne CTD 28/05/12 5 10 Depth (m) 15 20 25 30 35          Tripod  distance:  120  m   1510151515201525 Sound Speed (m/s)
  14. 14. Experimental Results Fused Track in Geographic Coordinates GPS ground-truth 42.835 Estimate 42.834 Fused Horizontal Range seen from Tripod 1Lat N (deg) 700 42.833 600 42.832 500 (m) 42.831 400 42.83 300 10.269 10.27 10.271 10.272 10.273 10.274 10.275 200 10.276 Lon E (deg) 5.446 5.448 5.45 5.452 5.454 4 Time (sec) x 10
  15. 15. Experimental Results Performances: 40 m max error over 700m range (6 %) Station 2 Station 1 GPS ground-truth Acoustic Estimate
  16. 16. Classification
RVM (Relevance Vector Machine)classifier•  Supervised statistical method (19 features selected)•  Binary•  Fully Bayesian model ⇒ provides probabilistic predictions•  No need of a-priori statistics•  Provides selection of most significant features•  Nice balance between simplicity and power
  17. 17. Feature extraction from data PSD function Spectrogram. 100 22 dB re. 1µPa/√Hz 90 80 Time (sec) 24 70 60 26 50 28 40 0 20 40 60 10 20 30 40 Frequency (kHz) Frequency (kHz) DEMON Spectrum X-PSD function 0.08 40 Normalized Amplitude 30 0.06 20 dB 10 0.04 0 0.02 -10 -20 0 50 100 150 200 0 20 40 60 Frequency (Hz) Frequency (kHz)
  18. 18. Classification results 10 Three  classes   8 Multi-class confusion rate matrix (%)Feature # 17 6 (Threshold = 0.5) Slow Fast Ships Pred 4 small small/ 2 boats mid- sized 0 15 10 20 40 True boats 5 0 Feature # 16 0 -20 Feature # 2 Slow boats 92 7.0 1.0 Slow, small boats Fast mid- 8.0 89.0 3.0 Fast small/mid-sized boats sized boats Ships (down to ferry size) Ships 0.0 0.0 100
  19. 19. Autonomous Sensing Vehicles•  eFolaga with e-nose
  20. 20. Complete MIS Integration AUV positions & E-nose data XML files To & From CNR MOOS HTTP Tracks Database & Aco features MOOS XML variables status update XML XML Style Transfor sheet mer
  21. 21. Integration with ARGOMARINE MIS
  22. 22. Acoustic Detection, Localization and Classification•  Concept successfully validated•  Advanced prototype system•  Possible exploitations: –  Marine mammal survey –  Monitoring of noise sources with important environmental impacts (wind-farm piling, regasification ships, etc.) –  Port protection
  23. 23. Test at sea•  May 2012: eNose mission integrated into ARGOMARINE MIS•  Sep. 2012: acquisition of sampling oil signals•  Nov. 2012: eNose missions with optimal sampling trajectory and real time MIS integration•  Cooperation with CNR-IFC, Graaltech and CNR-ISTI
  24. 24. OBJECTIVE: Find the optimum samplingdesigns for an AUV-mooring ocean observingnetwork•  METHODOLOGY: The problem is decoupled into a) finding the most adequate Floats Network•  sampling locations for the AUV and b) 
 -Unevenly distributed -Same cycling period -Synoptic measures to visit these locations in the fastest way. Glider Network•  Definition of a space filling design. Try to spread sampling locations throughout the region, leaving as few holes as possible. Sampling points are located to minimize a criterion •  Solution of the Travel-salesman Problem. Once the sampling locations have been defined, a trajectory of the AUV is computed to visit all the locations selected in the fastest way.
  25. 25. AUV mission plannerDefinition of Operational Constraints • Area • Time constraints • Vehicle speed • Number of vehicles • Obstacles Planning Module Space-filling Design Feedback between the space-filling design generator and the genetic algorithm until operational conditions Genetic Algorithm are satisfied. AUV Mission • Waypoints • Travelled Distance
  26. 26. Experimental Design forARGOMARINE Find an optimum mission for Folaga AUV to sample the selected marine area, considering the existence of a monitoring buoy and denied areas(red). Mission should take around 1 hr ( 1m/s)
  27. 27. AUV mission -Result for ARGOMARINE Optimum trajectory for the Folaga- AUV (dash-dot black line) compatible with operational constraints. The traveled distance is 2962 m.
  28. 28. WP4.5 Integration•  Current vehicle capabilities•  Macro Tasks –  Surface navigation –  Gliding mission –  Underwater navigation –  Idle –  Vertical Profiler•  User control mode –  Controlled by external software –  Simple command interface –  Complete control on devices•  Emergency –  Release drop weight balloon Valve pump Macro tasks and state machine
  29. 29. MOOS-IvP•  MOOS: Mission Oriented Operating Suite•  IvP: Interval Programming a mathematical programming model for multi-objective optimization•  MOOS-IvP is a set of open source C++ modules for providing autonomy on robotic platforms, in particular autonomous marine vehicles –  It provides a framework for data exchange/communication –  separation of overall capability into separate and distinct modules –  Front-seat/Back-seat concepts An Overview of MOOS-IvP and a Users Guide to the IvP Helm
 Michael R. Benjamin, Henrik Schmidt, Paul Newman, and John J. Leonard
  30. 30. Moos-IVP integration: E-Folaga Main controller (front-seat driver) Argomarine MIS Navigation Navigation commands data, 
 Heading, speed, depthvehicle status Definition of 
 TCP/IP communication GPRS/3G protocol Acoustic modem MOOS-ivp Shoreside station User Control Mode GPRS modem MOOS-iVp (back-seat driver) Radio modem MOOS DB: Log all variables Behavior examples n  Position –  Wait on position n  Comms –  Search pattern (lawnmower) n  Payloads data –  Goto location n  Mission status MOOS Set of –  E-nose mission on location database behaviors n  Etc. –  Go home
  31. 31. Many thanks to CMRE team•  Alberto A. •  Alberto G.•  Alessandra T.•  Federico C.•  Lavinio G.•  Piero G.•  Vittorio G.Alessandro, Marco, Salvatore, Piero
  32. 32. Conclusions•  Mission accomplished
  33. 33. Preamp. toHyd. FPGA 2-ch H.P. Filter* VGA* 24 bit ADC * pre-selected in hardware
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