Environmentally Adaptive Acoustic Sensing, Communication ...

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

mailto:[email protected]

http://lamss.mit.edu/

MIT Laboratory for


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


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


‘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


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 Depth Adaptation

for Optimal Communication and Navigation

Max depth

Forecasting ~ Acomms interval

Min pitch

MIT Laboratory for


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


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


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


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


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


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


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

Environmentally Adaptive Acoustic Sensing, Communication ...

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