A new technique to address CID and IFF studies

1. A new technique to address CID and IFF studies David Dean, Kathryn Hynd, Beejal Mistry, Alasdair Vincent and Paul Syms Dstl IMD and LSD 22 ISMOR, September 2005 Dstl/CP16723

2. Contents • Introduction and definitions • CID project background • the technical problem • Outline of the INCIDER model • decision engines • validation • Initial successes? • Questions

3. Introduction: What is amicide? • Definition of amicide (fratricide, friendly fire …): • “An attack by one or more initiators acting as a group on one or more friendly targets that are under friendly control” • Includes attacks that result in no casualties or damage • these are excluded in the US definition • A ‘near-miss’ is when firers nearly attack friends • but the error is realised before a shot is fired

4. Causes of amicides • Causes of 1167 20th. century events analysed by Dstl:

5. Consequences of amicide • Casualties to Blue forces • estimated at 10–20% of all casualties in WW1 and WW2 • greater in proportion if enemy less effective • Reduces tempo • including effects of lost opportunities to engage • Impact on morale • Political implications • nationally and within coalitions • used to undermine confidence in military

6. Introduction: What is CID? • UK definition of combat identification (CID): • “The process of combining situational awareness, target identification, specific tactics, techniques and procedures to increase operational effectiveness of weapon systems and reduce the incidence of casualties caused by friendly fire” • Thus there are 3 methods of improving CID: • situational awareness (SA) • physical target identification (TID) • tactics, techniques and procedures (TTPs)

7. The CID OA problem • MoD requires advice on CID cost-effective CID solutions • BoI across SA, TID and TTPs • across all environments – sea, land, air … joint and combined • spans the physical, information and cognitive domains • Cost-effectiveness implies quantitative modelling • cognitive domain usually addressed using ‘soft OA’ methods • No quantitative ‘off-the-shelf’ assessment tools available • Dstl understood all domains to a sufficient extent … • and was aware sufficient data existed to support modelling …

8. The INCIDER model

9. What is INCIDER? Integrates human, physical and operational domains A repository of parameters that will impact upon CID, and the relationships between them • The Integrative Combat Identification Entity Relationship Model • a generic representation of combat entities observing and identifying prior to engagement • can be tailored to represent all potential encounters where CID is a contributory factor

10. Operational domain • Scenario complexity • Context and RoEs • Possible target options • Physical domain • Organic sensor characteristics • Target characteristics • Environment – e.g. terrain, weather • Human domain • Pre-set characteristics • Variables, e.g. from training • Expectation, e.g. from briefings • Motivation • Physiology – e.g. stress and fatigue INCIDER conceptual overview INCIDER output: P(correct ID) and time to ID

11. Memory Absolute truth about identity Perception Retrieved information, reports “Picture” Organic and 3rd. party information Fusion process Decision output categories Aggregated information available to observer Detection Is it a military object? Recognition Is it a tank? Maximum information available to sensors Maximum information available to observer Action Should I kill it, report it, hide from it or ignore it? Identification What sort of tank is it? Total information available for decision CID decision-making scope Decision process Comprehension Projection Compiled view Battlespace Entity Decision

12. Stages in a typical encounter • Pre-conceptions • from plans, briefings and attitudes • Initial contact • target might be Red, Blue, White or a non-target • Build up confidence • by seeking additional information • Classify and decide • take action (outside current model)

13. Preconceptions 60% Enemy 75% Enemy 20% Neutral 20% Neutral 20% Friend 5% Friend Zone of Certainty 90% Friend 90% Enemy 5% Neutral 10% Neutral 5% Enemy 75 %Enemy 20% Neutral 100% Neutral 5% Friend 90 % Classification Range 50% Neutral 10% Friend 90 % Detection Range 40% Enemy 100% Friend 100% Friend

14. Initial Contact Something there, I think it’s hostile Combined SA and positional Errors Stale Friendly Position Report Maximum Range of Movement 100% Neutral

15. Build up confidence Advance Seek information from SA, EO, BTID etc. Send in a scout Contact HQ Check location Check SA Pause

16. Cognitive engines • Fusion engine tracks likely target identity • uses the Dempster-Shafer method • similar to Bayesian inference, but using ‘confidence masses’ • starts with target ID pre-conceptions • updated as new information received • Decision engine has two functions: • decides on further action before CID decision reached • decides on target identity when confidence threshold reached • INCIDER iterates loop until a CID decision is made

17. Situation awareness model Dempster- Shafer ‘fusion engine’ Battlespace target object Pre-set human parameters of decision maker Sensor model Confidence in target identity Variable human parameters of decision maker Iteration during run Decision outcome Decision engine Task selection Classification outcome Output: ID: X at time t Expectation/ history INCIDER decision model overview

18. Way ahead for INCIDER • Validation using synthetic environment • with psychometric testing of participants • collaborating with QinetiQ CHS and Land Division • Better modelling of possible errors • in physical, informational and cognitive domains • Aim to embed INCIDER in combat simulation • possibly Dstl’s Close Action Environment (CAEn) • will improve context, but may encounter interface problems

19. Validation: ‘model–test–model’ Questions? Live exercises SE modelling INCIDER model Validate Validates Generates Generates Calibrate, modify Vignettes Behaviour Human factors data Constructive simulation

20. Initial impressions and summary

21. Initial results • Results were intuitively ‘sensible’ • sensitive to different scenario vignettes • sensitive to physical, informational and cognitive factors • sensitive to interventions in SA, TID and TTPs • Different CID interventions helped in different vignettes • sometimes ‘binary’, other times more subtle influences • Interactions between some CID interventions seen • e.g. training and provision of specific TID equipments • statistically significant using ANOVAR

22. Initial successes • Quantitative assessment across three domains • enabling equitable comparison of different LoDs • contributes to understanding human factors in warfare • High levels of cross-disciplinary collaboration • technologists and engineers • military SMEs • psychologists • mathematicians … and to include cost forecasters • brought together by operational analysts

23. Summary • Dstl required to assess CID interventions quantitatively • sensitive to parameters in SA, TID and TTPs • sensitive to physical, informational and cognitive factors • Built the INCIDER model • and managed to provide acceptable ‘first cut’ data set • Substantial success from first results • sensitive to changes in scenarios and CID parameters • Contributes to understanding human factors in warfare • potential for application to other ‘fusion’ problems

24. Questions? Always keen to hear of amicide events for catalogue – compilation of V2.0 is ongoing– please e-mail me on prsyms@dstl.gov.uk