Transcript Homing Missile Guidance and Control at JHU/APL

Homing Missile Guidance
and Control at JHU/APL
SAE Aerospace Control & Guidance
Systems Committee Meeting #97
March 1-3, 2006
Uday J. Shankar, Ph. D.
Air & Missile Defense Department
240-228-8037; [email protected]
Unclassified
Abstract
This presentation discusses the GNC research at the Guidance, Navigation, and Control Group at the
Johns Hopkins University Applied Physics Laboratory.
Johns Hopkins University Applied Physics Laboratory (JHU/APL) is one of five institutions at the Johns
Hopkins University. APL is a not-for-profit research organization with about 3600 employees (68%
scientists and engineers). Our annual revenue is on the order of $670m. The Air and Missile Defense
Department is a major department of APL involved with the defense of naval and joint forces from
attacking aircraft, cruise missiles, and ballistic missiles.
The major thrust of the GNC group is the guidance, navigation, and control of missiles. Our mission is
to Integrate sensor data, airframe and propulsion capabilities to meet mission objectives. We are
involved with GNC activities in the concept stage (design, requirements analysis, algorithm
development), detailed design (hardware, software), and flight test (pre-flight predictions, post-flight
analysis, failure investigation).
The Advanced Systems section within the GNC group is involved with several projects: boost-phase
interception of ballistic missiles, discrimination-coupled guidance for midcourse intercepts, Standard
Missile GNC engineering, Kill Vehicle engineering, integrated guidance control, swarm-on-swarm
guidance, and rapid prototyping of GNC algorithms and hardware.
We discuss two examples. The first is the swarm-on-swarm guidance. This framework can be used to
solve guidance problems associated with several missile defense scenarios. The second is the
application of dynamic-game guidance solutions. This has applications in terminal guidance of a boostphase interceptor and the discrimination-coupled guidance of terminal homing of a midcourse
interceptor.
We discuss in more detail the problem of terminal guidance of a boost-phase interceptor. The problem
is formulated and a closed-form solution is offered.
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Divisions of
The Johns Hopkins University
School of Arts &
Sciences
Whiting School of
Engineering
School of
Professional Studies
in Business &
Education
School of Hygiene &
Public Health
School of Medicine
School of Nursing
Applied Physics
Laboratory
Nitze School of
Advanced
International Studies
Peabody Institute
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Profile of the
Applied Physics Laboratory
• Not-for-profit university research
& development laboratory
• Division of the Johns Hopkins
University founded in 1942
• On-site graduate engineering
program in 8 degree fields
• Staffing: 3,600 employees
(68% scientists & engineers)
• Annual revenue ~ $ 670M
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Air & Missile Defense
Advancing Readiness & Effectiveness of US Military
Forces
Key Programs:
− Cooperative Engagement Capability
− Ballistic Missile Defense
− Standard Missile
− AEGIS
− Area Air Defense Commander
− Ship Self Defense
Critical Challenge 1: Defend naval & joint forces from opposing aircraft, cruise
missiles, and ballistic missiles
Critical Challenge 2: Optimally deploy & employ multiple weapons systems to
maximize defense of critical assets such as military forces, civilian population
centers, airfields & ports in overseas theaters & in the United States
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GNC Group: Roles
Integrate Sensor Data, Airframe and
Propulsion Capabilities to Meet Mission
Objectives
GPS
•
•
•
•
Other
Sensors
Target
Motion
Missile
Seeker*
Guidance &
Navigation
Solution
Flight
Control
Guidance
Law
Missile
Motion
Intercept the Target
Maintain Stable Flight
Ensure Seeker Acquisition & Track
Minimize Noise and Disturbance Sensitivities
Airframe/
Propulsion
Primary Responsibilities
Cooperative Efforts
Autopilot
Loop
Inertial
Sensors
Homing
Loop
* Primary responsibility for seeker dynamics and radome effects
Concept
Development
•
•
•
•
System concept trade studies
GNC requirements analyses
Algorithm research
Real-time distributed
simulation
Detailed
Design
•
•
•
•
•
•
•
•
Component modeling
6 DOF development & verification
GNC algorithm development
Stability analysis
Flight control hardware testing
Evaluation of missile electrical systems
System performance analyses
Distributed simulation
Flight
Testing
•
•
•
•
Hardware-in-the-loop
Preflight performance prediction
Post-flight evaluation
Failure Investigation
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GNC Group: Current Efforts
Discrimination-Coupled Guidance
Boost-Phase Intercept Studies
and GNC Algorithm Research
RV, Booster, ACS,
Jammer, Decoys, …
Predicted Intercept Point
Uncertainty Basket
Contain Likely RV
Objects within
FOV
Terminal Homing
• Optimize KV fuel usage
• Satisfy hit requirements
Threat
Launch Point
Flyout Guidance
• Fixed-interval guidance
• Minimize KV handover errors
despite highly uncertain PIP
Radar
Track
Intercept Point Prediction
Maneuver to
Keep Likely
Objects
Within Divert
Capability
Standard Missile
• SM-3 Development
– INS/GPS analysis
– Flight control improvements
– 21” Standard Missile
•
SM-6/Future Missile Studies
– Inflight alignment
– GNC studies
•
Flight Test
– 6 DOF replication
– Failure investigation
– Hardware fault insertion
• Uncertain boost profile and
temporal events
Swarm-on-Swarm Guidance and Control Research
Sensor / detector element Engager Swarm
Track
Integrated Guidance
& Control (IGC)
Sensor / detector element
Lethal
footprint
Track
Designate
Designate
Target
Motion
KV G&C
• SM-3 Kill Vehicle
- Flight test
performance
assessment
- ACS design options
- Advanced pintle 6
DOF, G&C design
Asset
Autopilot
Target
Sensors
Guidance
Filter
Guidance
Law
Airframe /
Propulsion
Expected benefit of employing cooperative missile swarms is increased
performance robustness and mission flexibility
CVBG (Raid) Defense
Mitigate Raid Attack Vulnerability via
Cooperative Missiles
Missile
Motion
Inertial
Navigation
via dynamic-game optimization
Rapid GNC Prototyping
Analysis Simulation (not real-time)
Sensor
Signals
Fin
Commands
G&C
Algorithms
6-DOF
ASCM
CG CVN
Airframe, Sensor &
Environment Models
PC or UNIX processor
Rapid Prototype Testbed
G&C Real-time
Implementation
Processor 1
Sensor
Signals
Fin
Commands
Remaining
6-DOF
(real-time)
Processor 2
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Example GNC Research at
APL
Unclassified
Cooperative Multi-Interceptor Guidance
Multi-KV for BPI
Sea-Surface Asymmetric Adversaries (S2A2)
MiniMissiles
Threat
Trajectory
Uncertainty
Manage Information Uncertainty
via Increased Control Space
Threat
Launch Point
Modified
Aegis
Platform
Short time to ID & negate threat
Effect A Volume Kill Via
Increased Control Space
Speedboat Attacker Swarm
CVBG (Raid) Defense
Overhead Asset
Mitigate Raid Attack Vulnerability
via Cooperative Missiles
Swarm-Guidance: Expected Benefits
MaRV
• Eased centralized control requirements
- Remove “chokepoints, delays, etc.
• Reactive flexibility / adaptation to threats
• Scalability (response insensitive to #s)
• Near-simultaneous swarm negation
- Minimize chaotic threat response to being engaged
CVN
CG
ASCM
- Rapid battle-damage assessment and 2nd-salvo response
Swarm-guidance: Guide multiple cooperative missile interceptors to
negate one or more incoming threats (“Swarm-on-swarm”)
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Ballistic Missile Defense Challenges
Notional Sea-Based BoostPhase Intercept Scenario
Predicted Intercept
Point Uncertainty
Basket
Notional Midcourse-Phase
Intercept Scenario
Predicted Intercept Point
Uncertainty Basket
Terminal Homing
• Optimize KV fuel usage
• Satisfy hit requirements
Threat Launch
Point
Flyout Guidance
• Fixed-interval guidance
• Minimize KV handover errors
despite highly uncertain PIP
Radar
Track
Intercept Point Prediction
• Uncertain boost profile and
temporal events
Terminal Guidance - End Game
• Aimpoint Selection
• Satisfy Hit Requirements
Terminal Guidance
• Contain likely objects within FOV
• Volume / object commit
• Maximize containment
Flyout Guidance
• Cluster / volume commit
• PIP refinement / IFTU
• Energy / pulse management
Engageability / launch solution
• Predicted intercept point (PIP)
Boost-Phase Intercept Challenges
Midcourse-Phase Intercept Challenges
• Compressed timelines
• Uncertain threat trajectory, acceleration,
staging events and burn-out times
• Interceptor TVC has fixed maneuvering
time ending before intercept occurs
• Kill vehicle fuel and g limitations
• Complex threat cluster(s) act to postpone
identification of the lethal object
• Discrimination quality improves with time
• Divert capability decreases with time
• Guidance must generate acceleration
commands prior to localization of lethal object
Information uncertainty coupled with time and kinematic limitations pose
substantial challenges to ballistic missile defense
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Boost-Phase BMD: Terminal Homing
• Improve guidance law zero-effort-miss estimation accuracy
− This improves KV V and g-efficiency
• Assume that the threat acceleration increases linearly
− Improve on the APN concept versus a boosting threat
• Solve a dynamic-game (DG) optimization formulation
− DG framework provides robustness to threat acceleration uncertainty
− Couples the control components of the guidance problem to estimation and
prediction quality
− Control is less sensitive to threat acceleration uncertainties
• Accommodating threat burnout
− Employ a burnout detection cue (from the seeker)
− Use in estimation and guidance algorithms
• Derive closed-form solutions
− Prefer closed-form solutions to numerical solutions
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BPI Terminal Guidance Solution
Terminal Miss
Performance Weight
Terminal
Miss
Control
Uncertainties
General structure of the control solution
min max
b
1 tf
2
2
: J  r 2 (tf )   ( u(t ) R ( t )   2 w (t ) ) dt
2
2 0
u(t ) w (t )
Subject to:
1
ˆ (t )
u(t )  BT (t )Z(t ) I   2P(t )Z(t ) x
x(t )  A(t )x(t )  B(t )u(t )  D(t )w(t )
y(t )  C(t )x(t )  E(t )w(t )
Control Riccati
Equation Solution
Estimation
Uncertainty
1
Dynamic
Game Filter
Pk 1  Ak Pk1  CTk R k1Ck   2Qk  ATk  DkDTk , Pk 0  P0


xˆ k 1  Ak xˆ k  Bkuk  Ak Pk1  CTk R k1Ck   2Qk 

Relative
Position
Guidance
Law
Relative
Velocity
1

Threat
Acceleration
Qk xˆ k  CTk R k1  yk  Ck xˆ k 
2
Threat
Jerk


2
3
3  r (t )  v(t )t go  21 aT (t )t go  61 jT (t )t go
u(t )  2 
t go 3
 t g4o 2 
3
 3  1 

ˆ P(t ) ˆT

2 
3
2

84  t go 
 bt go 









ˆi
1 t
go

1
2
2
tgo
3 
tgo
6 
1
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
Unclassified
Thank You!
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