Download
c4isr analytic performance evaluation cape webinar n.
Skip this Video
Loading SlideShow in 5 Seconds..
C4ISR Analytic Performance Evaluation CAPE Webinar PowerPoint Presentation
Download Presentation
C4ISR Analytic Performance Evaluation CAPE Webinar

C4ISR Analytic Performance Evaluation CAPE Webinar

147 Views Download Presentation
Download Presentation

C4ISR Analytic Performance Evaluation CAPE Webinar

- - - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

  1. Henry Neimeier October, 2008 C4ISR Analytic Performance Evaluation CAPE Webinar

  2. Webinar Agenda • C4ISR Analytic Performance Evaluation CAPE concept brief • Command, Control, Communications, Computers, Intelligence, Surveillance, Reconnaissance • MILCOM 96 model demo • Precision Guided Munitions PGM Model Briefing & Demo • multivariate sensitivity analysis using Lonnie Library • Unclassified nominal data • Joint Dynamic Engagement Model JDEM Briefing • Military impact of space degradation • Analytica radar plots • Unclassified nominal data

  3. CAPE Concept • Analytic • Analytic network queuing and analytic risk evaluation • No discrete event statistical significance problem • Deterministic sensitivity analysis and parameter tuning • Short development cycle • Model evolution: daily modification • Analytica™ array abstraction capabilities • Appropriate aggregation: match data availability • Redland: summarize broad range of scenarios with distributions • Real time decision support • Execute on a portable in less than five minutes • Scroll through the multi-parameter space

  4. CAPE Principals • Model complex uncertain phenomena with aggregate probabilities • Represent uncertain environments with probability distributions • Processing, decision, execution, motion time distributions • Simple analytic calculations • No random number generation or sampling • Response surface inputs • Simulate simply • Fixed time step • Inputs for next time step are output expected values from previous

  5. Scenario Space Envelope • Identify key scenario characteristics that give rise to significant stress on C4ISR capabilities • Operational environment • Theater area • Terrain masking and delimitation • Foliage (urban) • Weather • Joint Suppression Of Enemy Air Defense JSEAD • Standoff operation • Attrition rates of sensors and attack platforms • Potential deployment rate • Infrastructure: communications, roads, air defense • Intelligence preparation of battlefield

  6. Dynamic CAPE Communication Improvements Sensor Improvements Processing Improvements Collection PEDS C2 Node Strike Platform Weapon CAPE Scope “Sensor-to-Shooter” Process More higher-value targets; greater likelihood of destroying target More information is collected, more frequently Probability of finding target is increased; sorties are more efficient More better-quality information is transmitted for processing; capacity and delay improvements Targets are identified more quickly Weapons are assigned more quickly Air Tasking Orders generated more quickly

  7. Dynamic CAPE Dynamic CAPE Overview Theater Environment Sensor Performance Range band:area,targets Operations tempo Terrain masking Weather, foliage EO/IR, SAR, MTI, SIGINT, HUMINT, Acoustic Sensed, Downlinked PEDS Target Characteristics Delay, Throughput Analyst capacity, delay Communications Point/Area collection Distributions: Move/Dwell time Target area CCD (fixed/moving) Weapon Allocation Target kills, value TLE & Engage Time Loss Ammo required, cost Logistic constraints Sensor Platforms Collection plan Number, Duty Cycle Coverage, CEP Downlink capacity Performance Targets at risk Process filter ___________ Categories: Scenario ISR architecture Target type Operations tempo Range band Cost Attrition PEDS,Ammo Sensors, Strike Platforms, Targets Attrition

  8. CAPE Tool Characteristics • Implemented in Analytica™ on PC • Employs analytic risk evaluation and analytic network queuing techniques • Overcomes several discrete event simulation limitations • Statistical significance • Causal chain, multi-parameter tuning • Fast model development employing library functions • Fast model execution: hundreds of runs per minute • All multi-runs in less than five minutes • Spanning scenario set • Arrow scroll through the multi-dimensional parameter space

  9. Discrete Event Simulation Time To Meet A Given Accuracy And Confidence Criteria T simulation time for a specified relative error  service time Ca interarrival time coe.variation Cs service time coe.variation Z unit normal deviate  utilization  tolerated relative error C E = 1 / P E = expected number of simulation events P = distribution tail probability C = uncertain model components

  10. Analytic Queuing As An Alternative To Discrete Event Simulation • Classical Assumptions • Steady state solution utilization <1 • Time to reach steady state increases as utilization increases (becomes infinite at unity utilization) • In practical problems loading changes before steady state is achieved • New approximation technique • Approximate transient solution • Utilization >1 build backlog that is worked off when utilization is less than 1 • Networks of queues (G/G/n Priority/No) • Calculation of factor effects in many parameter problems is now possible • Use baulking utilization to trade off delay time against throughput

  11. Analytic Risk Evaluation • Obtain the entire result probability distribution as a function of uncertainty in input parameter values • Entire probability distribution vice an uncertain estimate of the mean • Dynamic analytic risk evaluation (uncertainty flow from source to resultant measures of effectiveness) • Model a time varying workload and obtain uncertainty distribution over any time interval • Simplified parametric sensitivity analysis • Change in result per unit change in input factor • Maintain the causal chain between factor and result • Reduces model development and execution time

  12. Analytic Risk Evaluation Distributions • Beta distribution • Fit based on minimum, mean, maximum, standard deviation statistics • Sums and products of beta variates are beta distributed (close approximation, also works with functions) • Triangular distribution based on minimum, mode, and maximum statistics • Faster calculation used early in development • Method: • keep a running calculation of the minimum, mean, maximum, standard deviation, mode statistics through the calculation • fit probability distribution based on the above statisitics when desired

  13. Dynamic Analytic Risk Evaluation Example

  14. CAPE Application Examples • C4ISR Mission Assessment CMA-DISA • Seas - littoral task force operation-CNAN OPNAV • Littoral Combat Ship LCS-OPNAV • Crossroads - SA10/12 effectiveness-JCS J6,7,8 • Dynamic CAPE- daily platform, weapon, target pairing-DSC • ALMEM - Air Land Marine Engagement Model- AF GE97 • PEDS - Processing Exploitation & Dissemination-NGA • Fuse - multi-sensor fusion and tracking-DSC • CID - Combat Identification effectiveness-JTAMDO • JSF - Alternative joint strike fighter architectures-JSFPO • SIGINT performance-NSA • Broad Area Maritime Surveillance Analysis Of Alternatives AoA BAMS-OPNAV • Joint Dynamic Engagement Model- JNO, USJFCOM, JCS J8 • Airborne ISR – JCS J8 STRATCOM • ISR Air Space Trades- NSSO