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MINEFIELD MODELING ISSUES

MINEFIELD MODELING ISSUES. MINWARA 6 9-13 May 2004 Alan Washburn Naval Postgraduate School Operations Research Department (831) 656-3127 awashburn@nps.navy.edu. MY CONTENTION…. Clearing a minefield is a complicated process that should be aided by computers.

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MINEFIELD MODELING ISSUES

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  1. MINEFIELD MODELING ISSUES MINWARA 6 9-13 May 2004 Alan Washburn Naval Postgraduate School Operations Research Department (831) 656-3127 awashburn@nps.navy.edu

  2. MY CONTENTION… • Clearing a minefield is a complicated process that should be aided by computers. • The design of a clearance TDA will be heavily influenced by available data and the concept of the clearance process. • It is important to face certain issues early in development, rather than late.

  3. ABOUT MOWING THE LAWN • Why don’t we just “mow the lawn” and go home? • Radius of effects is not definite • Environmental variations • Buried mines • Mixed mines • Inherent randomness • Mine counters, probability actuators, sensitivity • Distractions

  4. NINE ISSUES • What will be in the database? • Sweeper casualties? • Mixed mine types? • Mixed sweep types? • Optimization or evaluation? • Input estimated mine numbers? • Geometry rectangular? • Sequential clearance? • Is it a game?

  5. pd Lateral range 1. SWEEP/HUNT DATABASE • Use A and B, where AB = area under lateral range curve • Or use the lateral range curve itself • Or avoid lateral range curves, as in • SL(sweeper)-TL(environment)>DT(mine)+noise • Or use a detailed simulation such as TMSS

  6. 2. SWEEPER CASUALTIES • Assume no casualties • Tremendous conceptual simplification • UMPM, NUCEVL, UCPLN, MEDAL,... • Or assume casualties have only economic implications • Replacements available immediately, at a cost • Decouples mine types • COGNIT, MIXER(opt) • Or permit casualties to potentially spoil the plan • Clearance plan only partially completed • Mine types not decoupled • BREAKTHRU, MIXER(sim)

  7. 3. MIXED MINE TYPES • Good tactics for the miner, commonly encountered (IRAQ) • Cheap generality if mine types are decoupled • Clearance is mainly a search problem • NUCEVL, UCPLN • Potential couplings between mines • Sweeper kills • Sweeper inefficiencies (time delays) • Threat to traffic (all mine types contribute)

  8. 4. MULTIPLE SWEEP TYPES • Order of entry important if sweepers are vulnerable • 7! = 5040 possible orders with seven sweep types • Can multiple types sweep at the same time? • Fratricide danger • Helicopters usually precede ships • Aid tactical choice of clearance type • Sweep or hunt? • Mechanical gear or sled? • Remote vehicles?

  9. 5. TACTICS OPTIMIZATION • Tactical questions • Track spacing • Time on task by sweep type • Equipment settings • Measures of effectiveness • Total clearance time (T) • Clearance casualties (C) • Target traffic casualties (H) • MIXER (opt) minimizes C + H subject to constraint on T • COGNIT minimizes C subject to constraints on T and H, etc.

  10. 6. ESTIMATED MINE NUMBERS • Number of mines may need to be an input • Required for most optimization issues • It is not true that SIT + clearance level = 1 • Number of mines notoriously random • Initial guesses will be WAGs • Need mine inventory database by country • Updating by evidence (Bayes) • MIXER requires mean and standard deviation by type • COGNIT assumes Poisson distribution

  11. 7. GEOMETRY Conventional rectangular (all but MEDAL) MEDAL allows arbitrary path orientations

  12. 8. SEQUENTIAL CLEARANCE • Conventional method has one plan, no feedback • Sequential method is to clear, observe, clear, … • Clearance achieved in stages • Plan for stage n + 1 depends on results in stage n • Summary statistics passed from stage to stage • MIXER optimizes within a stage, but not between • MEDAL passes summary statistics between stages • COGNIT and most other clearance programs are conventionally oriented

  13. 9. GAME THEORY • Minefield clearance is a competition • Mother Nature doesn’t make minefields • Why not use the zero-sum theory? • Clear opposition of interest • Mine sensitivity settings, for example, are an enemy choice not observable by the sweeper

  14. 10. ADVANTAGES OF GAME THEORY • Lack of requirement to guess things that cannot possibly be known, such as mine counter settings • Robustness of resulting tactics

  15. 11. DISADVANTAGES • Sensitivity to objective function • Optimal tactics may be mixed • “Flip a coin to decide whether to sweep or hunt first” • For a mine, “flip a coin to decide whether to detonate” • Computation is still an issue • But computers are getting faster…

  16. SUMMARY • Crucial decisions about clearance model should be faced early in development • Resulting software strongly affected • Effects are interrelated • ………………………….……..Questions?

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