environmentally adaptive acoustic sensing, communication
TRANSCRIPT
MIT Laboratory for
Autonomous Marine Sensing Systems
MIT Laboratory for
Autonomous Marine Sensing
Henrik Schmidt
Laboratory for Autonomous Marine Sensing SystemsMassachusetts Institute of technology
617-253-5727([email protected])
http://lamss.mit.edu
Environmentally Adaptive Acoustic
Sensing, Communication and Navigation
in the New Arctic
MIT Laboratory for
Autonomous Marine Sensing Systems
ICEX’16Integrated Acoustic Navigation and Communication
Acoustic Tracking and Navigation
Integrated with existing ARL/UW submarine tracking
WHOI HF Micro-Modem on platform emits tracking pulse
1PPS 3.5 ms CW at 13.5kHz with doublet every 10 seconds
10.5Khz carrier, 3kHz bandwidth 10ms FM sweep (“platform”) every 30 seconds
Topside tracker for aggressive outlier rejection, before fixes are transmitted back UUV via acoustic
communication to update onboard INS navigation solution
Upward-looking DVL fusion disabled due to ice motion of order .5 kn
Acoustic CommunicationHardware: WHOI MF Micro-Modem (3.5 kHz center, 1.25 kHz bandwidth), shared with NUWC
Digital Acomms transmit transducer and receive hydrophone
Software: Goby/DCCL marshalling, queuing, medium access, and physical layer interface.
DVL
INS
MIT Laboratory for
Autonomous Marine Sensing Systems
Acomms and Navigation in the Beaufort Sea
Then and NowRAP Corridors
Historical Beaufort Sea
Shallow source/receiver
provides direct paths to
target at all operational
depths out to 6 km
range.
No reliance on slower
surface duct multipaths.
Present Beaufort Sea
Shallow source/receiver
provides NO direct
paths to shallow target.
Ice interaction degrades
coherence beyond 0.5-1
km range.
Deeper CZ RAP paths
at 6-7 km range.
Deep target reachable
only at very short range.
MIT Laboratory for
Autonomous Marine Sensing Systems
‘Frontseat Driver’
Dynamic Control
Navigation
Sensors Actuators
Payload Autonomy
pHelmIvP
PlatformAutonomy
Acquisition
Array
Processing
Detection
Tracking
Classification
Main Vehicle Stack
SensorsModem
‘Backseat Driver’
Localization
Goby-2
DCCL
Data Bus
MOOSDB
pAcommsHandler
iFrontSeatMVC
Sensor Processing
And Control
Modeling
Forecasting
Virtual Ocean
Database/Modeling
pLamssMissionManager
Mission Autonomy
MOOS-IvP Payload AutonomyIntegrated Sensing, Modeling and Control
MIT Laboratory for
Autonomous Marine Sensing Systems
Environmental &Tactical Picture
IVP-Helm
Model-based Environmental Adaptation
Target @ 45 km
Ambient Noise
BHV_OptAcoustDepth
0 100
UtilityEmbedded
Acoustic Model
Weight
Weight
CONOPSPower, Coverage, Mission mode, Operational Constraints
DESIRED_DEPTH
Environmental
Database
Situational
Awareness
MIT Laboratory for
Autonomous Marine Sensing Systems
Autonomous Depth Adaptation
for Optimal Communication and Navigation
Max depth
Forecasting ~ Acomms interval
Min pitch
MIT Laboratory for
Autonomous Marine Sensing Systems
Simulated
Modem
Topside
Command and Control
Sensor Simulator
\
Simulated
Modem
Simulated
Modem
MOOS
Communication ModeluSimModemNetwork
Virtual Ocean
Network Simulator
pAcommsHandle
r
goby_liaison
MOOS
AUV #1
Mako
pHelmIv
P
Sensor
Simulator
Platform
Simulator
pAcommsHandle
r
MOOS
pProcessor
AUV #2
Hammerhead
pHelmIv
P
Platform
Simulator
pAcommsHandle
r
MOOS
pProcessor
uSimTowedArray
uSimPassiveSonar
uSimActiveSonarr
Environment
Environment
Environment
Ocean ModeluSimSoundVelocityMSEAS
Virtual Ocean
Environment Simulators
Acoustic ModeliBellhop
MSEAS
HYCOM
Bellhop
Environment
MOOS
Goby DCCL
Commands
Goby DCCL
Reports
Virtual Undersea Sensing Network
VirtuOSO
Virtual Ocean SimulatOr
MIT Laboratory for
Autonomous Marine Sensing Systems
MicroModem 0 Transmitted Signal
MicroModem 1 Received Signal
MicroModem 2 Received Signal
Real Time Modem Transmit/Receive Simulation
Virtual Ocean Network Simulator
Modem Hardware-in-the-Loop
MIT Laboratory for
Autonomous Marine Sensing Systems
Dynamic 3D Virtual Ocean
Mid-Atlantic Shelf SW06 MSEAS
AUV
Bottom-Mounted VLA
23 Elements.
D = 0.75 m
Frequency 900 Hz
Bandwidth 200 Hz
Source SPL 130 dB
Noise SPL 40 dB
SW06 MSEAS Hindcast
ACOMMS
MIT Laboratory for
Autonomous Marine Sensing Systems
Acoustic Communication Connectivity
Mid-Atlantic Shelfbreak
SW06 MSEAS
Bottom-Mounted VLA
23 Elements.
D = 0.75 m
Frequency 900 Hz
Bandwidth 200 Hz
Source SPL 130 dB
Noise SPL 40 dB
Autonomous Depth Adaptation
Depth 600 m
VLA
VLA
MIT Laboratory for
Autonomous Marine Sensing Systems
Environmentally Adaptive
Acoustic Communication Networks
• Scientific Issues
– Optimization metrics
• SNR
• Multipath Coherence
• Multipath minimization
• Array Signal Excess
– Environmental Updates
– Physical Apertures
• Array gain – Noise directionality
• Multipath resolution
– Adaptation Robustness
• Adaptation inherently unpredictable
• Collaborative adaptation autonomy – stability/robustness
• The Virtual Ocean is effective platform for exercising platform
autonomy with realistic environmental dynamics and timescales
MIT Laboratory for
Autonomous Marine Sensing Systems
Deep Sea Operations – DASH-Shark (DARPA)
Submarine Hold-a-Risk in deep ocean environments using a network of autonomous underwater
vehicles with active and passive sonars. Large area coverage (500x500 km) and persistance (90 days)
is achieved by combining state-of-the-art deep-rated vehicle technology and environmentally adaptive
sonar technology exploiting the mobility of the undersea network.
MORAN: Environmentally Adaptive Acoustic Communication (DARPA SBIR)
TEAC: Tactical Exploitation of the Acoustic Channel (DARPA)
Establishing and maintaining coherent acoustic connectivity between offshore autonomous
platforms and platforms and arrays on the continental shelf. MIT is responsible for the
environmentally adaptive platform autonomy on the nodes.
MOOS-IvP Autonomy and Virtual Ocean
Transitions
ICEX16/FAST – Future Arctic Sensing Technologies
(ONR/DARPA/COMSUBFOR)
Exploring hybrid horizontal/vertical array apertures for enhanced passive and
active acoustic surveillance in the new Arctic. Autonomous, Integrated
Sensing Modeling and Control for adapting the AUV survey to the dramatically
changing acoustic environment in the Arctic
Environmentally Adaptive Communication and Navigation in the Arctic
(ONR)
The MIT operation of a BF21 with a towed array in ICEX16 demonstrated the severe
underice acoustic environment created by the climate related enhancement of the
Beaufort Lens, a warm water lens entering from the Bering Strait, and which is neautrally
bouyant at 75 m depth, creating a distinct double-duct, which requires underwater
vehicles to be environmentally adaptive for maintaining acoustic connectivity.
MIT Laboratory for
Autonomous Marine Sensing Systems
Summary• Artificial Intelligence is critical to persistent and resilient operation of undersea distributed sensing
systems– Adaptation and collaboration may compensate for reduced sensor performance
– Communication channel inherently layered, highly band limited, latent and intermittent
– Integrated of sensing, modeling, and control required for sustained autonomous operation
• Nested, behavior-based autonomy is a key enabler – Nested modularity supports effective ’cloning’ of domain experts
– MOOS-IvP is open-source, highly portable autonomy software
• Multi-objective optimization HelmIvP is key enabler for adaptive autonomy.
• Provides 95%+ of leveraging autonomy software through nested repositories
• Provides templates for efficient application and behavior development by domain experts
– Adaptive sensing, communication and autonomy supported by embedded environmental and tactical modeling
• Robust and Resilient Onboard Data Processing– Processing products suited for machine decision making
– False Alarm Control is critical. Ocean is random!
– Robustness more critical than resolution!
• Virtual Experiments key to deployment of robust and resilient field systems– Adaptive autonomy is inherently unpredictable. Robust and resilient performance requires extensive testing with
actual autonomy software
– Requires high-fidelity, physics-based environmental simulation (oceanography, acoustics, dynamics).
– 500-1000 times more hours spent in virtual experiments than real ones for distributed sensing concept development.
– Hardware-in-the-Loop support• Stimulation of embedded processing chain
• Analog modem transmit/receive support