Emerging capabilities to integrate instruments on smallsats, airborne platforms and in situ devices into an intelligent, distributed observing strategy show great promise for measuring Earth science natural phenomena and physical processes that have not previously been characterized. To reduce the threshold for success in deploying such an intelligent, integrated observing strategy, a ground-based testbed system is proposed. Virtually all of the technologies needed for using such a tool have matured to the point of being used, individually. Virtually none of the technologies have been deployed, working together. The technologies to be deployed should be integrated into a working "breadboard" where the components can be debugged and performance and behavior characterized and tuned-up. A system of this complexity should not be expected to work without full integration and experimental characterization. Further, and perhaps more importantly, in order to successfully propose a space-based element to this strategy, teams must convince the relevant science community that the risk is low enough to warrant the investment. The main benefit of the testbed is to retire the risk of integrating these new technologies and increase the Technology Readiness Level (TRL) of each component as well as the System Readiness Level (SRL) of the integrated system

Cole, Marge

Le Moigne, Jacqueline

Little, Mike

NASA Technical Reports Server

1TESTBED REQUIREMENTS TO 
ENABLE NEW OBSERVING 
STRATEGIES
05/21/2019
Mike Little
Jacqueline Le Moigne
Marge Cole
Advanced Information Systems Technology (AIST) Program
https://ntrs.nasa.gov/search.jsp?R=20190028349 2019-09-26T20:07:40+00:00Z
2ESTO/AIST Objectives
• AIST
– Advanced Information Systems Technology Program
– Part of the Earth Science Technology Office (ESTO) in 
NASA Science Mission Directorate (SMD)
• AIST Goals
– Reduce the risk, cost, size, and development time of 
Earth Science Division (ESD) space-based and 
ground-based information systems; 
– Increase the accessibility and utility of science data; 
and 
– Enable new observation measurements and 
information products.
3Current Observing Strategy
4New Observing Strategies (NOS)
New Observing Strategies: 
• Multiple collaborative sensor nodes producing measurements integrated from 
multiple vantage points and in multiple dimensions (spatial, spectral, temporal, 
radiometric)
• Provide a dynamic and more complete picture of physical processes or natural 
phenomena 
5NOS Environment
NOS will:
• Utilize Distributed 
Spacecraft Missions (DSM), 
i.e., missions that involve 
multiple spacecraft to achieve 
one or more common goals.
• Coordinate Space 
Measurements with Aerial 
and Ground Measurements.
Technology advances have created an opportunity to make new 
measurements and to continue others less costly, e.g., Smallsats equipped with 
science-quality instruments and Machine Learning techniques permit handling 
large volumes of data
NOS Goals: 
o Enable new science measurements 
o Improve existing science measurements
o Reduce cost of future NASA missions
6DSM/ICC and Sensor Webs
A special case of DSM is an Intelligent 
and Collaborative Constellation (ICC) 
which involves the combination of:
• Real-time data understanding
• Situational awareness 
• Problem solving
• Planning and learning from 
experience
• Communications and cooperation 
between multiple S/C.
A Sensor Web is a distributed system of sensing nodes (space, air or ground) that are 
interconnected by a communications fabric and that functions as a single, highly 
coordinated, virtual instrument. It semi- or - autonomously detects and dynamically 
reacts to events, measurements, and other information from constituent sensing nodes 
and from external nodes (e.g., predictive models) by modifying its observing state so as 
to optimize mission information return. (Note: a “communications fabric” is a 
communications infrastructure that permits nodes to transmit and receive data between 
one another) (e.g., EO-1 SensorWeb 3G). (Ref: Steve Talabac et al, 2003)
7NOS Drivers
• Respond to new Earth Science Decadal Survey – Measurement-based:
o Utilize multiple modes (wavelengths, spatial, temporal res), multiple vantage 
points, etc. to create a unified picture of a physical process or natural 
phenomenon
o Reduce costs: large flagship missions only when needed, leverage first existing 
govt and commercial assets, ground sensors, UAVs, balloons, instruments on 
ISS and CubeSats
• Create an “internet” of sensor data, from models up to in-orbit assets, via all 
intermediate levels:
o Link WWW to Space-Internet
o Link to other networks (e.g., DARPA Blackjack)
o Provide interoperatibility-accessibility with/to large flagship missions
o Future: link Earth SensorWeb to Helio SensorWeb to Lunar SensorWeb to 
Martian SensorWeb, etc.
• Create an analog-like system to test future lunar, Mars or deep-space sensor 
webs and constellations
• Societal Applications:
o Respond quickly, on-demand to unexpected events (hurricanes, volcanoes, 
etc.)
o Leverage “out of network” assets for emergencies (DOD-, NOAA-, Foreign-, 
etc.)
8NOS Enables Earth Science
• Improved Models that can Drive Observations
o Integrate models with in situ, airborne and orbital instruments
o Continuously running models direct the observation system in 
collecting data
o Real-time targeting of transient and transitional phenomena
o In situ triggering of observing system
o Train configuration prolonging observation of an event
o Viewing an event from multiple angles
o Autonomy in focusing the observational system on the event
• Coordinated arrays of sensors (station keeping)
o Reduce error with statistics
o Improve resolution with multi-node instruments in phased arrays
o Viewing of phenomenon from multiple angles and directions
9New Observing Strategies (NOS) – Measurement 
Acquisition (“Mission” Design or Model-Driven)
New 
Measurement 
Requirement/ 
Request
Are there 
existing assets 
satisfying this 
request?
YES
In-Situ Sensors
1 or Several 
Satellite Sensors 
SensorWeb if 
Several of them 
Used/Targeted in 
Coordination
NO
Should one or 
several existing 
asset be 
supplemented?
In-Situ Sensors
1 or Several 
Satellite Sensors 
SensorWeb Working 
TogetherYES UAV’s, Balloons, HAPS, 
ISS Instrument
NO
Should a new 
observing system be 
designed 
independently from 
existing assets?
YES
Monolithic 
Spacecraft
Distributed 
Spacecraft Mission
DSM with 
Coordinated 
Spacecraft/Sensors
Combination of Ground, Air 
and Space Instruments 
Working Together
SensorWeb
Working Together
10
Event of Interest 
observed by an 
existing space 
sensor or group of 
sensors (Ground, 
Air and/or Space)
New Observing Strategies (NOS) – Observation 
Planning or Rapid Response to Event of Interest 
This sensor or one of 
these sensor(s) is re-
targeted to ”follow” 
the event
Another space sensor 
or group of sensors 
is/are re-targeted to 
”follow” the event
A UAV(s) flight plan is 
being scheduled to 
complement space 
observations
Ground data are being 
acquired to 
complement space 
and air observations
Data and 
Information Fusion Replanning
11
• Hydrology
o River flow and Flooding 
o Snow fall in 3D
o Aquifer degradation
• Precipitation
o Extreme precipitation events
• Cryosphere
o Glaciers changes
o Sea Ice changes
• Urban Air Quality Events
o At a resolution (vertical and 
horizontal)
• Biodiversity
o Migrations 
o Invasive species
o Transient spring phenomena
• Solid Earth and Interior
o Landslides
o Plate movement
o Volcanic activity
o Interior magma movement
• Disaster Management
o Floods
o Earthquakes
o Volcanic Eruptions
NOS for Candidate Science Needs
12
Flood Monitoring with Space and Ground Sensors
1. A weather forecast or radar image indicates the potential for flood-
inducing precipitation.
2. This triggers a network of ground-based sensors measuring changes in 
overland flow to begin telemetering data at high frequency.
3. When the ground sensors detect change in overland flow, they trigger 
a series of additional measurements:
a. Space-based measurements, e.g., combination of space-based 
optical and radar, to determine surface water extent.
b. In-situ measurements taken by either USGS technicians or future 
in-situ or UAS-mounted sensors, to measure high water level.
c. A constellation of radar CubeSats is tasked to take targeted multi-
angle measurements
Example of an Hydrology Use Case
13
NOS Testbed Goals
• Technologies to be deployed should be first integrated 
into a working breadboard where the components can 
be debugged and performance and behavior 
characterized and tuned-up.  
• A system of this complexity should not be expected to 
work without full integration and experimental 
characterization
Testbed Main Goals:
1. Validate new NOS technologies, independently and as a system
2. Demonstrate novel distributed operations concepts
3. Socialize new DSM technologies and concepts to the science 
community by significantly retiring the risk of integrating these 
new technologies.
14
NOS Testbed – Concept of Operations
The NOS Testbed consists of multiple sensing nodes, simulated or actual, 
representing space, air and/or ground measurements, that are interconnected 
by a communications fabric (infrastructure that permits nodes to transmit and 
receive data between one another and interact with each other). Each node is 
supported by hardware capabilities required to perform nodes monitoring and 
command & control, as well as intelligent “onboard” computing. The nodes 
work together in a collaborative manner to demonstrate optimal science 
capabilities.
The testbed is built in a modular fashion with well-defined interfaces so that 
each of its components (e.g., sensors; inter-node communication model, 
technique and protocols; inter-node coordination; real-time data fusion and 
understanding; planning; sensor re-targeting; etc.) can be replaced, tested and 
validated without modifying the rest of the testbed.
The testbed has the capability to interact with various mission design tools, 
OSSEs and one or several forecast models. It will demonstrate the science 
value of a concept, what we could do with it and what we could not do 
otherwise.
15
NOS Testbed Phased Execution
The NOS Testbed is built in such a way that it can be incrementally augmented 
and improved with additional sensors and capabilities. It will have multiple 
phases, e.g.:
• Phase 1 with only multiple satellite-simulators, i.e., actual or simulated data from 
ground stations Level 0 data and/or software simulated satellite data
• Phase 2 integrating in-situ sensors with satellite simulators
• Phase 3 integrating in-situ sensors and satellite simulators with UAV’s and balloons
• Phase 4 integrating actual CubeSat(s) with the previous sensors
• Phase 5 could include international collaborations and coordination.
DSM/SWOS/NOS technologies and operation concepts would then be ready to 
transition and actually be infused into actual Science missions.
Experiment Lifecycle (for a given phase of the testbed):
1. Experiments are proposed under various mechanisms
2. For a given proposed Experiment:
a. Governance Board tentatively approves Experiment
b. Primary requester works with the Testbed Support Contractor to produce test plan, test 
script and cost estimate, including in-kind contributions, if any
c. Primary Requester negotiates cost estimate with Governance Board
d. Governance Board approves the Experiment and its funding
e. Experiment is run and results are published
f. The Primary Requester presents the results and any lessons learned to the Governance 
Board
g. Based on (f), Governance Board decides to update the testbed accordingly, if needed
16
Experiment Considerations
• Inputs from all relevant stakeholders
– Anticipate conflicting objectives
• Good Experiment Design
• Combine experiments into a single set of 
experiments
• Permit separation of proprietary from public 
experiments
• Ensure all data is collected and authenticated
• Ensure key data are evaluated in real-time to 
ensure any variations/corrections are collected
• Ensure Science stakeholders are satisfied of the 
value of an NOS Strategy
17
NOS Testbed – Framework Components
Sensing Nodes can be represented at different remote locations and by:
• Level 0 data received at ground stations
• Simulated data derived from actual Level 0 data
• In situ sensors
• UAV’s equipped with one or several sensors
• Balloons equipped with one or several sensors
• High Altitude Pseudo-Satellites (HAPS), high flying UAV’s
• CubeSats carrying one or several sensors
Computing Hardware will be available at each node, e.g.:
• AIST-14 and -16/French Simulator
• AND/OR Actual Hardware:
• Raspberry PI (RPI)
• CHREC CubeSat Space Processor (CSP)
• SpaceCube 2.0, etc.
• Neuromorphic Chips: SBIR/RBD (Palo Alto) or Intel Loihi or IBM TrueNorth (actual if 
available or simulators)
Software Framework, e.g.:
• Flight Software: core Flight System (cFS) running at each node
• Distributed Messaging System – middleware for communicating between nodes, e.g., 
combination of SBN, DTN, 0MQ, SpaceWire
• Data System – RTAP
• Command and Telemetry System – COSMOS
18
NOS Testbed – System Architecture
Decision Making/
System Executive
(onboard and/or on the ground)
Sensor Data
Processing
Observation 
Forecasting/
Targeting
Planning & 
Scheduling
Sensor Data
Integration & 
Analysis
Intelligent Data
Understanding
Navigation/
Maneuvering/
Pointing
ANALYSIS onboard 
and/or on the ground)
ACTION PLANNING
IN&S 
and GPS
Sensors
Control
Sensor Data
Acquisition
SENSING
•Optical,
• Multispectral
•Hyperspectral
• IR, Microwave, etc. 
• Stereo,
• Lidar, 
• Range, …
Sensor Pool
• Long-Term Data Archives
• Historical Data
•Calibration/Validation Data
• Science Findings
• Maps
• Previous Missions, …
Knowledge Base
• Data Assimilation
• Science Products
• Prediction
• Modelling
Science
Inter-S/C 
Comms
S/C – Ground
Comms
COMMUNICATIONS
Requests
19
Some Specific NOS Required IS Capabilities
Developing Information Systems Technologies to Create a Unified and 
Dynamic Pictures of Science phenomena with NOS Architectures
NOS
Constellations 
and SWOS
Onboard Data 
Understanding
Inter-node 
Communications
Cybersecurity
Autonomous 
Onboard Planning
Spacecraft 
and Sensor 
Re-Targeting
Spacecraft/Sensor/Data 
Ontologies and 
Interoperability StandardsSeamless 
Interaction to 
Science Models
Multi-Source Data 
Fusion and Integration
Large Constellation 
Mission Operations
Measurement 
Observations Concepts 
Design
20
NOS Testbed Benefits to Earth Science
• Reduce risk of new technologies and/or novel integration 
of multiple technologies => increase TRL of each 
technology and SRL of the integrated system
• Thoroughly test all the implications of various mission 
concepts by linking the testbed to mission design tools, 
OSSEs and models
• Compare several competing technologies in a 
standardized framework
• Develop confidence in the Earth Science Community in 
the use of integrated observing strategies
21
Backup
22
Sensor Webs for Tracking Events of Interest 
User 
Community
L1/L2 & GEO
Satellites
Winds Lidars,
Stereo Imagers,
RADARs,
Temp. & Humidity,
Sounders
CubeSats, 
Balloons, 
UAVs
Ground 
Sensors
Models and 
Predictions
Multi-Nodes from 
Multiple Organizations: 
NASA, DOD, OGAs, 
Industry, Academia, 
International
23
General DSM Required Capabilities
Launch and Deployment
• Low-thrust propulsion
• Low-cost deployment 
multi-spacecraft systems
Conceive and Design, Design and 
Development Tools
• Pre-Phase A/Phase A DSM mission design tools 
• Prototyping & Validation testbeds
• Model-based engineering tools
Build and Test Manufacturing, I&T and 
Assembly
• Develop/extend standards 
• Integration and Testing (I&T) frameworks
Operate Communications
• High-speed S/C to S/C 
comms
• Low-cost & fast SmallSats
uplink/downlink
Operate GN&C
HW & SW for:
• Autonomous sensing & control
• Absolute & relative navigation
• Coordinated pointing
Operate Ground Data Processing
• Multi-spacecraft mission ops 
Centers and ground data systems 
• Solutions for DSM “big data” 
operations challenge
Analyze Onboard Intelligence
• Onboard recognition of events of interest
• Onboard goal-oriented planning & 
scheduling 
• Autonomous re-targeting and 
reconfigurability
Analyze and Share Science Data Processing
• Scalable data management for large DSM
• High accuracy multi-platform calibration, 
registration & fusion
We Start with Science …
... and End with Science
24
• Technology Community Resistance
– Lack of familiarity and a new direction
– Leisurely pace of technology development in Government environments
• Science Community Resistance
– Perceived risk of new measurement techniques and unproven technologies
– Lack of confidence
• Flight Community Resistance
– Consider a mission to be an entire suite of observations of a phenomenon
– New Mission Ops Model with autonomy and management
• Confidence must be built within the Science and Flight Communities
– Conduct well designed experiments early and get a science team buy-in
– Demonstrate components at earliest possible date and keep using them
– Communications plan to reduce the lack of familiarity
– InVEST experiments and demonstrations
– Open conversation, not isolated experiments
Cultural Change is Needed
25
ESTO Distributed Testbed
• Not a single facility, rather a collaboration among a wide range of participants
– Each has something to contribute
– Each could benefit by experimenting in the open (non-proprietary)
– Emulated in situ, airborne and orbital assets
• Funded to conduct early-stage experiments and demonstrations
– Competitively selected
– Agree to commitment to making contribution available
• Governance
– Self-managed, prioritized and coordinated
– Lifetime expected 5 years
– Annual Review
• Types of testing initially
– Remote operation of instruments among different parties at different sites
– Deconflict commands and schedules
– Block-chain distributed ledger applications
– Protocol for quick reaction agreement to share facilities (think seconds instead of months)
– Control and Monitor system needs
• Demonstrate some specific science use cases
– Emulate several science missions with different science communities
• Elevate at least two nodes into space (Compete in INVeST Program)
– Impact of latency, international agreements, etc.,
– Perform experiments to debug, revise, estimate performance and demonstrate integration
26
References
• CubeSat Space Processor (CSP), 
https://www.spacemicro.com/assets/datasheets/digital/slices/CSP.pdf
• Real-Time Application Platform (RTAP), 
http://www.tnbrqats.com.my/rtap/ and http://www.tnbrqats.com.my/wp-
content/uploads/2017/09/TNBR-Tech-Profile-021-Real-Time-
Application-Platform.pdf
• Core Flight System (cFS), https://cfs.gsfc.nasa.gov/
• SpaceCube 2.0, https://spacecube.nasa.gov/
• Zero-M-Queue, 0MQ, Distributed Messaging,  
http://zeromq.org/intro:read-the-manual
• Delay/Disruption Tolerant Networking (DTN), 
https://www.nasa.gov/content/dtn
• Software Bus Network (SBN), https://software.nasa.gov/software/GSC-
16917-1
• COMSOL MultiPhysics Software, https://www.comsol.com/
27
2003-2013 / SensorWeb Model
Higgins, G., Kalb, M., Lutz, R., Mahoney, R., Mauck, R., Seablom, M., Talabac, S., 2003: 
“Advanced Weather Prediction technologies: Two-Way Interactive Sensor Web & Modeling System”


Testbed Requirements to Enable New Observing Strategies

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