Autonomy Architecture for a Raven Class Telescope with Space

Situational Awareness Applications 3nd US Chinese Technical Interchange, Beijing

May 15-17, 2013

Ryan D. Coder, Graduate Research Assistant Marcus J. Holzinger, Assistant Professor

School of Aerospace Engineering, Georgia Institute of Technology

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Space Situational Awareness

• SSA is [Joint Publication 3-14]

– SSA involves characterizing, as completely as necessary, Resident Space Objects (RSOs)

• Needs are articulated by

– National Space Policy (2010)

– DoD National Security Space Policy (2011)

• Helps to ensure [Joint Publication 3-14]

– Space flight safety

– Protecting economic interests

– Protecting space capabilities

– Protecting military operations and national interests

– Implementing international treaties and agreements

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Why SSA is Hard

• Data deprived [Sabol et al. 2002 & Nielsen et al. 2012]

• SSN sensors not centrally controlled [Nielsen et al. 2012]

• Increased # of data product customers [Nielsen et al. 2012]

• Air Force analyst staffing issues [Weeden 2012]

N A T I O N A L S E C U R I T Y S P A C E S T R A T E G Y U N C L A S S I F I E D S U M M A R Y

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“The now-ubiquitous and interconnected nature of space capabilities

and the world’s growing dependence on them mean that irresponsible

acts in space can have damaging consequences for all of us.”

- 2010 National Space Policy

Space is vital to U.S. national security and our ability to understand emerging threats,

project power globally, conduct operations, support diplomatic efforts, and enable global

economic viability. As more nations and non-state actors recognize these benefits and

seek their own space or counterspace capabilities, we are faced with new opportunities

and new challenges in the space domain.

The current and future strategic environment is driven by three trends – space is

becoming increasingly congested, contested, and competitive.

Space is increasingly congested. Growing global space activity and testing of China’s

destructive anti-satellite (ASAT) system have increased congestion in important areas in

space. DoD tracks approximately 22,000 man-made objects in orbit, of which 1,100 are

active satellites (see Figure 1). There may be as many as hundreds of thousands of

additional pieces of debris that are too small to track with current sensors. Yet these

smaller pieces of debris can damage satellites in orbit.

THE STRATEGIC ENVIRONMENT

Figure 1. Source: Joint Space Operations Center

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5000

10000

15000

20000

25000

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62

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66

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68

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70

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72

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00

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02

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06

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08

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Nu

mb

er o

f O

bje

cts

Total

Debris

Uncataloged*

Payloads

Rocket bodies

2000

Total: 9,600

1980

Total: 4,600

2010

Total: 22,000

* Uncataloged= unknown object and/or unknown origin

1990

Total: 6,900

1970

Total: 1,800

Iridium-COSMOS Collision

COSMOS 2421 Breakup

Chinese ASAT Test

Shemya Radar to full-power ops

Satellite Catalog Growth

Source: JSpOC, DoD 2011 National Security Space Policy

Category of Sensor

Near Earth (NE)

Deep Space (DS)

Dedicated ~ 25 % ~ 90 %

Collateral ~ 70 % ~ 5 %

Contributing ~ 5 % ~ 5 %

AF Space Surveillance System (AFSSS)

Eglin

Diego Garcia Ground-Based Electro-optical Deep Space Surveillance (GEODSS)

Moron Optical Surveillance System (MOSS)

Globus II

Socorro GEODSS

Maui GEODSS

Maui Space Surveillance System (MSSS)

Cobra Dane

Reagan Test Site (RTS)

Millstone/ Haystack/Auxiliary

Ascension

Thule

Clear

Cavalier

Beale Cape Cod

Fylingdales

UNCLASSIFIED

UNCLASSIFIED

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Need for Autonomy in SSA

Dull, repetitive tasks:

• Modern systems make hundreds of observations nightly [Sabol et al. 2002]

• Developing observation schedule complex

Fast timescales:

• Objects cross telescope field of view in seconds [Shell 2010]

• Dynamic local environment motivates near real time local schedule repair (e.g., weather)

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Overview

• Motivation

• Telescopes

• Autonomy

• Proposed Architecture

• Example

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Raven-class Telescope Overview

• Started as AFRL R&D effort

• Combination of COTS hardware and software

• Many Ravens currently in operation

• 1 Raven at Maui Space Surveillance Site contributes to SSN (Sabol et al. 2002)

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Other Autonomous Telescopes

LANL RAPTOR [Verstrand et al. 2008] NASA MCAT [Mulrooney et al. 2010]

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Overview

• Motivation

• Telescopes

• Autonomy

• Proposed Architecture

• Example

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• Two common cognition models – Observe, Orient, Decide, Act (OODA) loops developed by Col. John Boyd

[Boyd 1976]

– NASA Goddard developed Plan, Perceive, Act (PPA) loop [Truszkowski et al. 2009]

Cognition Models

OODA PPA 10

Control Loop as a Cognition Model

Reference Generation

Controller Actuator

Processing

Sensor Processing

Filter / Estimator

Decide Orient

Observe

Act

Real World

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Autonomy Architectures

Intelligent Machine Design

Levels Autonomy Architectures

NASA IMD [Truszkowski et al.

2009]

3 Layer [Alami et al. 1998]

DARPA/ISO SARA [Lewandowski

et al. 2001]

JPL CLARAty [Estlin et al. 2001]

Reflection Planning Mission Decision

Routine Executive Hardware -

Reaction Functional Cyber Functional

Reflective Agents have the ability to learn Routine Agents have the ability to evaluate & plan Reactive Agents interface with hardware (e.g., control loops)

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Machine Learning Background

Categorized by type of feedback available [Russell and Norvig 2009]:

• Supervised

– Learns function to map input-output pairs

• Reinforcement

– Agent rewarded or punished for actions taken

• Unsupervised

– No explicit feedback provided

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Constraint Satisfaction Problems

Cast dynamic scheduling problem as CSP [Russell and Norvig 2009]:

• Solved using general purpose heuristics

• Partial sets that violate constraints removed

• Utility function used to select best alternative

Used extensively in space applications:

• Hubble [Johnston 1990]

• Chandra [Brissenden 2001]

• Spitzer [Tyler et al. 2008]

• EO-1 [Sherwood et al. 2007]

– Identify opportunistic science

– Prioritize data downlinks

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Overview

• Motivation

• Telescopes

• Autonomy

• Proposed Architecture

• Example

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Distributed Sensor Networks

Centralized Decentralized

Centralized Mission Planner

Sensor Sensor

Static Task Generation

Dynamic Scheduler

Dynamic Scheduler

Current Space Surveillance Network [Hill et al., 2010]

• Uses knowledge of covariance • Limited sensor knowledge

• Limited covariance knowledge • Excellent sensor knowledge

Distributed Mission Planner

Distributed Mission Planner

Distributed Mission Planner

Robustness & Complexity

Centralized superior when minimizing overall catalog covariance [Hobson et al., 2011]

Decentralized superior to current SSN [Jayaweera et al., 2011]

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Proposed Autonomy Architecture

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Agent

Raven Class Telescope

Central Planning

Agent

… Other networked sensors

Increasing Machine

Intelligence

Reflection

Routine

Reaction

Commanded Objectives

Agent Agent

Agent Agent Agent

Agent Agent Agent

Space Object Catalog

Overview

• Motivation

• Telescopes

• Autonomy

• Proposed Architecture

• Example

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Space Object Detection

• To detect a Space Object, need an SNR ~ 6

• Biggest factor without a model: Atmospheric

Transmittance! • Goal: Autonomously estimate transmittance for local

azimuth and elevation over short time periods to enable local schedule repair / improvement

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Atmospheric transmittance

AllSky340 640x480 KAI-340 CCD F/1.4 Fujinon fisheye lens

SQM-LU-DL HWHM: 10deg

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Desired Information

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High Transmittance (SNR)

Low Transmittance (SNR)

Learning with Response Surface Methodology

Challenges: • Lack of first-principles model for local micro-climate

• Computational effort

Approach: • Physics-based response surface equations [Kirby 2001]

• Catalog star observations selected intelligently using DoE [Box and Draper 1987]

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Example Autonomy Architecture

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CSP Agent

Functional Agent

RSM Agent

Raven Class Telescope

Central Planning

Agent

… Other networked sensors

Increasing Machine

Intelligence Reflection

Routine

Reaction

Commanded Plan

Space Object Catalog

Raven Example Continued

All-Sky Camera Brightness Sensor

Autonomous DoE to observe impactful catalog stars

Empirical Function Fit (RSM)

Empirical Probability of Detection

SO of Interest

Can then use pdetect as an input to a CSP scheduler

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THANK YOU

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