argomarine final conference - cmre-nato - stefano fioravanti, alessandra tesei
DESCRIPTION
A presentation about the Area Access Surveillance Technologies in the field of ARGOMARINE ProjectTRANSCRIPT
Area Access Surveillance Technologies
S. Fioravanti, A. Tesei November 21, 2012
Outline • CMRE history and mission • Underwater Monitoring Technologies • Hardware design and development • Detection and Localization • Classification • Experimental results • Conclusions
• 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
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
UW ModemPassive acoustic
Passive acoustic
Autonomous sensing
Acoustic Monitoring
Underwater Monitoring Technologies
3D VIEW
Shore Lab
Acous&c: Triangula&on among distributed sensors
Cell-phone GPS
Major differences: • Complex environment
• Noise & Sound propagation • Variety of unknown sound sources (blind monitoring)
EM waves
Acoustic waves
Examples of noise from vessels
Time (sec)
Freq
uenc
y (kH
z)
Slow, mid-size, leisure boat. Spectrogram (dB re. 1µPa)
0 1 2 3 4 5 6 7 8
5
10
15
20
25
30
40
50
60
70
80
90
100
110
Time (sec)
Freq
uenc
y (k
Hz)
NURC rubber boat. Spectrogram (dB re. 1µPa)
5 6 7 8 9 10 11 12 13
5
10
15
20
25
30
40
50
60
70
80
90
100
110
Time (sec)
Freq
uenc
y (k
Hz)
Mid-speed, small ship. Spectrogram (dB re. 1µPa)
0 1 2 3 4 5 6 7 8
5
10
15
20
25
30
40
50
60
70
80
90
100
110
High level @ LF Limited prop. cavitation Few spectral lines
High level in Wide Band Strong prop. cavitation Several spectral lines
High level @ LF & MF No cavitation Many spectral lines
Exploitation of time coherence of signal received by each sensor pair of each array
Requirements: • Sparse hydrophones (d>>λ) • High sampling frequency
τ: Time Delay α : Bearing angle
α = acos ( τ cw / d )
Slant Plane
Hydrophone Pair
α d
Hyd 1 Hyd 2
ΔT
Hyd 1
Hyd 2
τ X-Correlogram EF2 Tripod 1 - Pair 23
Time (sec)
Bear
ing
(deg
)
0 10 20 30 40 50
0
50
100
150
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
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
Localization from one station and from two stations
y
x
z
Azimuth Eleva.on
k
Water D
epth
P
Two Tripods: 2D Triangulation
One Tripod
Tripod1
Tripod2
Top View
θ1
θ2
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 equipped with GPS antenna
– Integration of GPS ground-truth position data into acoustic data files
• Blind acoustic monitoring
GPS antenna
1510 1515 1520 1525
5 10 15 20 25 30 35
Sound Speed (m/s)
Dep
th (m
)
CTD 28/05/'12
Tripods deployment: • 40m depth • About 450-550m off shore • Sandy-posidonia seabed • Relatively quiet environment
Tripod distance: 120 m
No evidence of thermoclyne
Enfola (Elba Island) test site
10.269 10.27 10.271 10.272 10.273 10.274 10.275 10.276 42.83
42.831
42.832
42.833
42.834
42.835
Lon E (deg)
Lat N
(deg
)
Fused Track in Geographic Coordinates
GPS ground-truth Estimate
5.446 5.448 5.45 5.452 5.454 x 10 4
200
300
400
500
600
700
Time (sec)
(m)
Fused Horizontal Range seen from Tripod 1
Experimental Results
Station 2 Station 1
GPS ground-truth Acoustic Estimate
Experimental Results Performances: 40 m max error over 700m range (6 %)
ClassificationRVM (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
Feature extraction from data
0 20 40 60
40
50
60
70
80
90
100
Frequency (kHz)
dB re
. 1µ P
a/√
Hz
PSD function
0 20 40 60
-20
-10
0
10
20
30
40
Frequency (kHz)
dB
X-PSD function
50 100 150 2000
0.02
0.04
0.06
0.08
Frequency (Hz)
Nor
mal
ized
Am
plitu
de
DEMON SpectrumFrequency (kHz)
Tim
e (s
ec)
Spectrogram.
10 20 30 40
22
24
26
28
Classification results
Slow small boats
Fast small/mid-sized boats
Ships
Slow boats 92 7.0 1.0
Fast mid-sized boats 8.0 89.0 3.0
Ships 0.0 0.0 100
Multi-class confusion rate matrix (%) (Threshold = 0.5)
Pred
True
Three classes
-200
2040
05
10150
2
4
6
8
10
Feature # 2Feature # 16
Feat
ure
# 17
Slow, small boats
Fast small/mid-sized boats
Ships (down to ferry size)
Autonomous Sensing Vehicles
• eFolaga with e-nose
Complete MIS Integration
MOOS Database
HTTP
XML files To & From CNR
XML Transfor
mer
XML Style sheet
MOOS variables
Tracks &
Aco features
AUV positions &
E-nose data
XML status update
Integration with ARGOMARINE MIS
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
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
OBJECTIVE: Find the optimum sampling designs for an AUV-mooring ocean observing network • METHODOLOGY: The problem is decoupled into
a) finding the most adequate • sampling locations for the AUV and b)
to visit these locations in the fastest way.
• 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.
Floats Network
-Unevenly distributed-Same cycling period-Synoptic measures
Glider Network
AUV mission planner Definition of Operational Constraints
• Area • Time constraints • Vehicle speed • Number of vehicles • Obstacles
Planning Module
Space-filling Design
AUV Mission
• Waypoints • Travelled Distance
Genetic Algorithm
Feedback between the space-filling design generator and the genetic algorithm until operational conditions are satisfied.
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)
Experimental Design for ARGOMARINE
Optimum trajectory for the Folaga-AUV (dash-dot black line) compatible with operational constraints. The traveled distance is 2962 m.
AUV mission -Result for ARGOMARINE
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
Macro tasks and state machine
balloon Valve
pump
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 HelmMichael R. Benjamin, Henrik Schmidt, Paul Newman, and John J. Leonard
Moos-IVP integration:
Behavior examples – Wait on position
– Search pattern (lawnmower)
– Goto location – E-nose mission on location
– Go home
E-Folaga Main controller
(front-seat driver)
MOOS-ivp User Control Mode (back-seat driver)
Set of behaviors
Navigation data,
vehicle status
Navigation commands Heading, speed, depth
Acoustic modem
GPRS modem
Radio modem
Argomarine MIS
MOOS database
MOOS DB: Log all variables n Position n Comms n Payloads data n Mission status n Etc.
Shoreside station MOOS-iVp
Definition of communication
protocol
TCP/IP GPRS/3G
Many thanks to CMRE team
• Alberto A. • Alberto G. • Alessandra T. • Federico C. • Lavinio G. • Piero G. • Vittorio G. Alessandro, Marco, Salvatore, Piero
Conclusions
• Mission accomplished
Hyd. Preamp.
H.P. Filter* VGA* 2-ch
24 bit ADC
to FPGA
* pre-selected in hardware