AIAA@Infotech 2012
Presented by: Dick Stottler stottler@StottlerHenke.com
650-931-2714

• Resources: ground & space-based radar & E/O sensors
• Dedicated, Collateral, Contributing
• Plan/schedule observations (metric/SOI) of known objects, & surveillance
• Currently tracking tens of thousands of objects
• Automatic capability needed for DCS/SSA scenarios
• Inputs: tracked object observation opportunities and desired tracking/metrics/SOI/objectives, surveys and time constraints
• Output assignment of resources to tasks for time periods, if possible
• During potential attacks/problems, rapidly adjust (replan)

• Antiquated/unique computer HW/software/scheduler at sensor
• Prevents Automated Real-Time Response (Human responds)
• Prevents (or at least hinders) globally optimized scheduling
• Non-dedicated sensors, sharing with other missions
• Non-Taskable sensors (e.g. space fences)
• Balancing need: complementary metric info & SOI & surveys
• Probabilistic results (calculated probability of successful track)
• Includes both prob. of attempting & prob. of detecting given attempt
• Heterogeneous/complementary sensors (radar: accurate range/range rate; EO accurate angles & angle rate
• Complementary signature info. (RCSs vs. spectral intensities)
• Orbit accuracy needs different sensors, different parts of orbit

• Orbiting object described by 6 orbital parameters:
• Eccentricity, Semimajor Axis, Inclination, Longitude of ascending node, Argument of periapsis, and Mean anomaly at epoch
• Or by 3-D position and 3-D velocity vectors
• Radar systems provide
• accurate range and range rate measurements (2 #s)
• Less accurate angle & angular rate (4 #s w/ relatively high error)
• Optical sensors provide
• good angle (2 parameters) and angular rate (2 parameters)
• but no range or range rate (2 parameters missing).
• For each sensing opportunity of an object from a sensor:
• based on geometry & sensor accuracies: 6 x 6 covariance matrix
• the variances and covariances of the errors in measurement
• describes the probable volume of space

• C1 & C2 are covariances (volumes) from 2 measurements
• C3 is result of
• combining them
• C3 = C1*(C1 + C2 )-1 *C2

• Volumes intersect near
• orthogonally
• Volumes intersect with narrow angle

• Summary: Current Tasker implements policies dictated for it
• Local scheduling done with no global info.
• Must be suboptimal (can ’ t consider bottlenecks or other sensor data being collected)
• Priority based local scheduling (IS suboptimal)
• Executing all scheduled tasks likely implies undertasked
• Possible to get sensor ’ s other mission ’ s detailed tasking?
• No automated real-time response
• Human response required
– no automated feedback loop
• Number of objects increasing (new sensors / smaller object resolution)

• Scheduling with resource assignment is NP Complete (next slide)
• Takes exponential time to guarantee an optimal solution
• (4 options per each choice) 1000 Decisions
• 4 1000 = 2 2000 = 10 200 >> 10 80 = # particles in the universe
• Much scheduling research is in Job Shop Domain
• Choice of resource doesn't effect start time: not applicable for satellites
• Constraint Satisfaction Problem: Search for Solution
• Search Problem: Genetic Algorithms, Simulated Annealing, A*,
Heuristic Search, Iterative Repair
• Operations Research: Linear Programming, Branch and Bound, Hill-
Climbing, Usually these must oversimplify the problem
• Use multiple algorithms and pick the best schedule
• Common Bad Algorithm: Priority Order, Greedily Pick Resource
• Other ways to guarantee high priority tasks, e.g. swap out lower Priority
• Nearly Linear Algorithms (Global Info.) vs Search

• 3 Remaining Tracks: Track 1, Track 2, Track 3
• Track 1 highest priority, Track 3 lowest
• 2 Sensors: A (1 track capacity left), B (2 tracks left)
• A best for Track 1, also applicable to Track 3
• B Applicable to Tracks 1 & 2
• Priority-based allocation poor:
• Track 1 <- A, Track 2 <-B, Track 3 <- ?
• # of solutions: 2 (Track 1) x 1 (T2) x 1 (T3) = 2
• But, 30 tracks, 4 choices each:
• 4x4x4x…x4 = 4 30 >million trillion solutions
• Avoid simple linear & systematic (exponential) algs
• Avoid “ greedy ” decisions made without global context
• NP Problems: solutions hard to find, easy to verify

• Resource Contention (Bottlenecks):
• Scheduling flexible, high priority activity on a specific resource at a specific time, when other (probably less desirable) options are available when a lower priority activity absolutely needed it.
• Both could have been scheduled but only one is
• Need to consider resource side (not just task)
• Need global (schedule difficulty, resource contention) information when making local decisions
• Precalculate most contended-for resources (Global Info.)
• Process most constrained requests first
• Swap out lower priority tracks, if necessary
• Global Info also includes complementary data needs for most accurate orbit metrics

• Simple priority-based schemes will significantly underutilize resources (including information-gain-based schemes)
• LEO objects – highly constrained to specific sensors at very specific times (10 – 15 minute windows)
• GEO objects – specific sensors, any time
• MEO & non-GEO DS objects – loosely constrained in both
• Highly Elliptical Orbit (HEO) – A mix of all 3
• Complex requests:
• Min/Preferred tracks/day
• Min/Max time or orbits between tracks
• Mix of resources (e.g. 2 optical, 3 radar /day)
• Precalculate most contended-for resources
• Process most constrained requests first
• Swap out lower priority tracks, if necessary

• Current Problem
• Currently LEO FR: all-pass codes ( “ Every sensor, every pass ” )
• Duplicative and wasteful
– Multiple sensors engaged over continental US, very little time separation/benefit
– Small debris sensors (SDS) (SHY, EGL, CAV) waste time/energy on these
• Need to reduce drain on SDSs & improve overall efficiency
– For each object, apply all-pass code tasking only to those sensors needed to meet a certain stated revisit time, but not assigning every sensor to every object
• Solution: AI-based scheduler to meet revisit times & schedule small debris tracking

• Different situation from LEO
• No fixed definition of “ pass ”
• More interest in frequent revisit for this regime
• Sensor set more flexible
• Ground vs space-based optimization needed
• More elaborate and potentially satisfactory solutions possible
• Solution: AI-based scheduler to revisit appropriately
• Need to use predicted tracks from surveillance tasks
• Consider sunlight on sensors and objects
• Allocate tracks appropriately to ground radar & ground and spacebased optical sensors

• Schedule all objects ’ tracking (LEO and DS)
• Use all sensors (new, surveillance/untasked, collateral, contributing)
• Quality Metric for DS: Covariance statistics
• Quality Metric for LEO: number of tracks per object per day(empirically linked to accuracy)
• Other Metrics: meeting revisit times, tracks lost, missed detections, redundant tracks, searches conducted, sensor utilization, SOI data gathered, etc.
• Solution: AI-based scheduler, Bottleneck avoidance, FOV scheduling
• Combination of 4 subschedulers (DS/NE, FR/not)

• Global, optimizing scheduler composed of 4 subschedulers
• Deep Space Frequent Revisit Scheduler
• Low Earth Orbit Frequent Revisit Scheduler
• Deep Space Non-FR Scheduler
• Low Earth Non-FR Scheduler
• All have in Common:
• Use of AI Architecture
• LOS constraint between satellite and sensor
• Optimize a Metric (Covariance, # of tracks, time between tracks)
• Large # of satellites (100s to 1000s)
• Bottleneck Avoidance (massive overlapping version)
• Set of satellites / “ free ” tracks / set of resources

• Generating a schedule much more difficult than grading one
• Architecture makes development of different algorithms quick
• Need different algorithms for different problem spaces
• Different amounts of time available
• Call best scheduler for problem space & time available
• Call multiple subschedulers, grade results, return best

SSN Scheduling Application
Interface
Tasks, Resources, Constraints Descriptions (XML) Schedule (XML)
Preprocessor
Emergency
Response
Scheduler
Bottleneck
Avoidance
Scheduler
Simplex
Method
Scheduler
...
Different
Algorithm
Scheduler
Resource Selection, Planning, Constraint Satisfaction, &
Scheduling Methods and Objects

dsd
Track/Search
Requests, Visabilities
Sensor Status
Task
Conversion
Preprocessor
Human
Directives
Human
Override &
Influence
Allowed
Process
Task
Task Queue
Task Ordering:
Pick a Task
Select/Assign
Resources
Task, & Time
Window
Schedule objects
Handle
Problems
Propagate
Constraints
Fix Problems &
Optimize
Schedule
Task Conversion
Knowledge
Criteria &
Instructions
Methods &
Knowledge
Guidance
Problem
Resolution
Knowledge
Knowledge Editors

• SSN Scheduling is difficult, complex, and hampered by antiquated hardware
• Essentially 4 different problems need 4 different but integrated solutions
• DS FR Scheduler: intelligently use of sun/EO/radar/space-based-EO
• DS NonFR Scheduler should utilize complementary observations to minimize orbit covariances
• NE FR Scheduler; “shield” SDSs, and more efficiently use other sensors while meeting FR reqs
• NE non-FR scheduler: maximize # of observations for each object
• All need to use predicted tracks from non-taskable sensors
• Use general AI architecture to rapidly create needed subschedulers
• Can set priorities/observation #s to “schedule” unschedulable sensors to nearest 90 minute period

• With sensors that can track more than one object and move their field of FOV, need to schedule the sensor ’ s aimpoint
• To maximize the number of tracks
• Very hard problem: FOV pointing adds 2 continuous dimensions
• Calculate and consider sets of aimpoints that can track multiple objects each

Candidate & Selected Aim Points