Developing a Prototype Ground Station for the Processing, Exploitation, and Dissemination of pleo Sensor Data

August 11, 2021

Developing a Prototype Ground Station for the Processing, Exploitation, and Dissemination of pLEO Sensor Data

Presented By: Jen Wilbur (SciTec, Inc)

Debi Rose

Dan Rossiter Paul Wood

Jen Wilbur

David Simenc Eric Principato Travis Williams Jason Hamant

Matthew McHugh Sander Malmquist John Maloney

Outline

2

• Introduction

• OPIR Demo

• SAR Demo

• EO/IR Demo

• Conclusions

Introduction

• SwRI and teammates SciTec, and Amazon Web Services (AWS) demonstrated a novel, commercial processing, exploitation, and dissemination (PED) prototype to Air Force Space & Missile

Systems Center (SMC).

• Objective: Demonstrate a low-latency, horizontally-scalable, PED capability featuring cloud-based processing of data

collected by future payloads sensing in multiple modalities hosted on commercial spacecraft and downlinked through commercial gateway injection points. Demo delivery of

processed data to tactical users at forward operating locations.

• Addresses a lack of established gateways or processes to ingest data collected from DARPA’s BLACKJACK-capable spacecraft and distribute that data through a commercial gateway to a

warfighter in theater.

PED Prototype

PED Overview - Three OPIR Sensors

4

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R e c e i v e

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AWS S3 data storage –Encrypted Frames

CLI casino2-aft

CLI casino2-fore

AWS Oregon Region – us-west-2

AWS Ohio Region – us-east-2

Encrypted Sensor Frame Data Files

Data Forwarder

Decrypt, virus scan

Raw Sensor Frames with MetaData

AWS GovCloudRegion

US-gov-west-1 Simulated

‘High Side’

AWS S3 data storage – Decrypted Frames

Visualization (VEGA)

GovCloud– Encrypted Frames

Data TransportMission Data Processing

Frame Processor

Correlation/ Fusion Processors

2d tracklets 3d tracks

Frame Processor

Frame Processor MDP Ingest/ Balance

Full-frame

streaming data

Unified Data Library

Data Forwarder Data Forwarder

Assess ability to transport and process full frame, full rate OPIR data from multiple sensors

Warfighter surrogate

Scene Simulation Geometry: OPIR Demo (3 Sensors)

Parameter Value

PRA Terrain DB Korea 300m PRA Cloud DB Kiev

Local Time 12:00 (noon) Sensor off-nadir

angle

27^o

Satellite mean anomaly spacing

9.35^o

Sensor size 2048x2048 px

Sim Time 165 s

Frame rate 20 Hz

Frames per sensor 3300 frames 3 body-fixed sensors were simulated on 2 spacecraft.

Spacecraft were separated to maximize boresight overlap

Simulated Data For the OPIR 2k x 2k Demo

Simulated data provided a wide range of realistic conditions.

Target Signatures

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Scenes w/ Targets

Transmitter (CLI) – decommutates the CCSDS encoding, initiates the transport process

‘Receiver’ – lambda function - signals forwarder when complete file is available in Receiver region S3

Forwarder – receives files from receiver S3, decrypts, virus scans, places un-encrypted file in Forwarder S3 bucket ready for MDP ingestion

CASINO PED – Data Transport Pipeline

R e c e i v e

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AWS S3 data storage –Encrypted Frames AWS S3 data storage –Encrypted Frames

Data Forwarder Data Forwarder Data Forwarder

AWS S3 data storage –Decrypted Frames

Mission Data Processing (MDP) Data Ingestion

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AWS GovCloud – us-gov-west-1 AWS Oregon Region – us-west-2

AWS Ohio Region – us-east-2

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OPIR PED End-to-End Timeline

8

• Average end-to-end timeline for OPIR 2kx2k processing from receipt at gateway to delivery to Mission Data Processing in GovCloud = ~11sec

• End-to-end time includes all data transfer (Xfer), Overhead (OH), and Processing

Test Date CLI Rcv Fwd Total (avg) Min Max

8 Jan 2021 2.46 0.93 1.15 4.54 3.03 16.59

8 Jan 2021 2.66 0.93 2.56 6.20 3.59 34.00

Test Date CLI Rcv Fwd Total (avg) Min Max

8 Jan 2021 2.21 0.92 1.15 4.29 3.29 12.31

8 Jan 2021 2.25 0.93 2.64 5.82 3.65 21.33

OPIR 2kx2k DT Processing Times (seconds)

OPIR 2kx2k Xfer Times (seconds) OPIR Nominal Test Run Summary

High Level OPIR MDP Architecture

3. BKG Processor – Ingest raw calibrated full-frame imagery, perform clutter suppression, and output clutter-suppressed full-frame imagery

4. TDE Processor – Ingest clutter-suppressed full-frame imagery, perform track-before-detect processing, output 2D tracklets

5. CORR Processor – Ingest 2D tracklets from multiple sensors, perform multi-sensor measurement correlation, output associated measurements

6. FUS Processor – Ingest associated measurements, perform state vector estimation, output 3D tracks

7. Data analysis/App Dashboard – Interface for

executing and running the CASINO MDP in AWS as well as for analyzing and visualizing results

Full Frame Interface

1. Data Ingest – Read CASINO data frames transported to AWS GovCloud region storage

2. Data Balancer – Route and balance CASINO data to MDP elements

1

3 4

5

6 7

2

As Demonstrated End-to-End MDP Timing (2k x 2k)

10

MDP: Elastic Scaling to 30 Sensors (10X Demo)

Single Sensor SAR Demo Overview

12

MDP Processing

Encrypted Sensor Frame Data Files

Data Forwarder

MDP Ingest/ Balance

Decrypt, virus scan

Raw Sensor Frames with MetaData (Simulated binary data format for SAR)

‘Low Side’

CASINO simulated binary data → (encrypted → CCSDS packetized

→ TCP/IP) CASINO simulated binary data

(sensor 2 → (encrypted )

AWS GovCloud Region US-gov-west-1

Simulated

‘High Side’

AWS S3 data storage – Encrypted Frames

GovCloud– Encrypted Frames

AWS S3 data storage – Decrypted Frames

Receiver

POP – IP routing to AWS Cloud Resources

AWS US-West-2

Oregon (CONUS)

Data Prep/

CLI

Starlink –

‘User Terminal’

Local POP

SpaceX Equip Rack

Starlink –

‘Gateway’

Redmond WA or Los Angeles CA

Area

Ka Downlink– >10 Gbps

Downlink of

Sensor Data from Satellite

CASINO simulated binary data → (encrypted → CCSDS packetized

→ TCP/IP)

Limited Uplink Bandwidth

baseline 10 Mbps

Internet Connection Commercial Cloud Data Center

(Oregon)

Data TransportMission Data Processing

Original Purpose/Goals:

• Assess ability to transport and process SAR Data – including transport across Starlink connection

SAR Mission Data Processing Overview

SAR MDP Architecture

14

CASINO PED Overview: Single Sensor EO/IR

Encrypted Sensor Frame Data Files

Data Forwarder Decrypt, virus

scanRaw Sensor Frames with MetaData (Simulated binary data – EO/IR format)

AWS Direct Connect (100 Gbps)

‘Low Side’

Ground Antenna Site/ Local AWS Data Center

(Bahrain on OCONUS)

AWS Ground

Station AWS – ME-south-1

(OCONUS)

CASINO simulated binary data – EO/IR format → (encrypted → TCP/IP)

CASINO simulated binary data – EO/IR format (sensor 2 → (encrypted )

AWS GovCloudRegion

US-gov-west-1 Simulated

‘High Side’

Data Prep / DemoCLI

AWS S3 data storage – Encrypted Frames

AWS S3 data storage – Decrypted Frames

R e c e i v e r

AWS US-West-2

(CONUS)

Visualization (VEGA) EO/IR Ingest and

Balance

Automatic Target Recognition (ATR)

CCSDS Decomm occurs prior to transfer to Receiver

Commercial Cloud Data Center (Oregon)

GovCloud– Encrypted Frames

Data TransportMission Data Processing

Original Purpose/Goals:

• Assess ability to transport and process EO/IR data

EO/IR Demonstration MDP Summary

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Conclusions

• The SwRI Team demonstrated a robust, low-latency solution capable of transporting, processing, and delivering data received from a LEO satellite to a secure cloud

processing center, where MDP algorithms were applied to produce actionable information products for the Warfighter

• As expected, transfer times are dependent on distance from destination and speed of the network

• We successfully demonstrated MDP of OPIR data that keeps up with the full-frame data rates

expected for “next generation” sensors (low latency), can be scaled elastically to accommodate data streams from multiple sensors (30 demonstrated), and that is modular – supporting

multiple missions

• Our PED prototype successfully transported and processed multi-mission data collected by a variety of pLEO sensor types to output information relevant to the Warfighter

• OPIR – output 3D tracks in Tactical message formats

• SAR – processed I/Q data to generate intuitive, information dense, images

• EO/IR – Ingested high spatial resolution imagery and processed with an AI neural net-based ATR algorithm with cloud masking to enable robust detection and classification of objects of interest