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Intelligent Space Surveillance Network (SSN) Scheduling 2012 Presented by:Dick Stottler 650-931-2714.

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Presentation on theme: "Intelligent Space Surveillance Network (SSN) Scheduling 2012 Presented by:Dick Stottler 650-931-2714."— Presentation transcript:

1 Intelligent Space Surveillance Network (SSN) Scheduling AIAA@Infotech 2012 Presented by:Dick Stottler 650-931-2714

2 Space Surveillance Network Scheduling 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)

3 SSN Scheduling Challenges 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

4 Sensing Error => Covariances 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

5 Combining Measurements/Covs C1 & C2 are covariances (volumes) from 2 measurements C3 is result of combining them C3 = C1*(C1 + C2 )-1 *C2

6 2 Examples, Complementary & Not Volumes intersect near orthogonally Volumes intersect with narrow angle

7 Current SSN Scheduling Problems 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)

8 Scheduling 101: 1 Slide! 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

9 VERY Simple NP Completeness eg 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

10 Schedule Issue: 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

11 SSN Track and Search Scheduling 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

12 LEO Frequent Revisit Scheduler 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

13 DS Frequent Revisit Scheduler 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 space- based optical sensors

14 GIG-Enabled Global Optimizing Scheduler 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)

15 Family of 4 SSN Schedulers 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

16 Multiple Subschedulers 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

17 High Level Scheduling Architecture

18 AI Architecture

19 Conclusions 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

20 FOV Scheduling 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

21 Object Track & Visibility Squares

22 Intersecting Visibility Sets

23 Intersection Set Clusters Candidate & Selected Aim Points

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