Objective POD Estimation

1
Objective POD Estimation

• The Development of a Standard Method
• For Gathering and Using Detection Data

• R. Quincy Robe Jack Frost

2
Presentation Outline

• Define and Describe a detectability index
• Show how it is used with other data to estimate
  POD
• Describe a Procedure for doing detection
  experiments to determine a detectability index

3
The Detection Process

• A series of glimpses as the searcher moves
  through the environment containing the object.
• Detection with any one glimpse depends on the
• Search Object (size, color, contrast, etc.)
• Environment (weather, terrain, vegetation, etc.)
• Search Resource (sensor and platform)
• Distance from the Resource to the Object

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What is Probability of Detection (POD)?

• Applies to some amount of area (e.g., a segment)
• Probability of detecting an object if present
• POD is a function of
• Effort (Resources, Search Speed, Time)
• Size of the Area covered
• Search object detectability

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What is Effort?

• Total Distance traveled by searchers while
  searching in the segment
• Effort searcher speed x time x number of
  searchers
• What is Area covered?
• Size of the area over which the searching effort
  is approximately uniformly spread

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What is Detectability?

• How can one measure or quantify how easy or hard
  it will be to detect a particular object with a
  particular type of resource (sensor) in a
  particular environment?

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What about Maximum Detection Range?

• Easy to measure directly.
• Measures how far from the sensor an object can be
  detected by an alerted searcher who knows where
  to look.
• Does not address whether the object will be
  detected within that range.
• Does not measure how much detecting can be
  expected from a searcher (sensor).
• No simple, predictable correlation with detection
  performance.

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What about direct estimation?

• Humans are very poor at estimating probabilities
  of any kind.
• Compare
• How many of 10 objects would you have found?
• How many of 10 objects could you have missed?
• No such thing as one size fits all POD for
  everything from small clues to large objects.
• Direct estimation Wild Guess

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Effective Sweep Width (Koopman)

• Cannot be measured directly
• Is an objective measure of detectibility
• Large value gt easy to detect
• Small value gt hard to detect
• Depends on the characteristics of
• Searcher/Sensor (What we are searching with.)
• Search Object (What we are searching for.)
• Environment (What we are searching in.)
• Terrain, Vegetation, Weather, etc.
• Has units of length (feet, meters, miles, etc.)

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A Uniform Random Distribution

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Effective Sweep Width
(Unrealistic Ideal Sensor Making a Clean Sweep)

Number detected 40. Number missed within sweep
width 0. Number detected outside sweep width
0.
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Effective Sweep Width
(More Typical Sensor)

Number detected 40. Number missed within sweep
width 16. Number detected outside sweep width
16.
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Effective Sweep Width Notes

• In both of the previous examples, there were
• The same object density ( of objects/unit of
  area),
• The same length of searcher track, and
• The same number of objects detected (40).
• Therefore,
• The effective sweep widths are also the same.
• Effective sweep width represents the expected
• amount of detection.

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Lateral Range (Koopman)

• Distance to right or left of sensor at the
  closest point of approach (CPA)
• Lateral range curve

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Effective Sweep Width

• Key to Improved Search Planning and Evaluation
• Improves POD Estimation
• Allows us to Objectively Relate POD to Effort
  Expenditure
• Has both Predictive and Retrospective Value
• More Accurate and Reliable than Subjective
  Estimates
• Based on Observable Factors
• Improves Effort Allocation
• Makes known, proven (mathematical) techniques
  available
• Improves conceptualization of the search problem

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Southern California
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Southern California
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Western Washington State
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Western Washington State
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Objective POD EstimationFor a searched segment

• Effort z Total Distance Searchers Cover
  search speed ? time ? number of searchers
• Effective Sweep Width W from detection
  experiments
• Area Effectively Swept z ? W
• Coverage C
• POD 1 e-C (Koopman)

Area Effectively Swept
Area of Searched Segment
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POD vs. Coverage Graph (Koopman)
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Uncorrected Effective Sweep WidthsIn Nautical
Miles For Aerial Search Over Land (IAMSAR Manual)
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Effective Sweep Width Correction FactorsFor
Aerial Search Over Land (IAMSAR
Manual)(Multipliers)
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Sweep Width Issues for Ground Search

• Too many different types and combinations of
  terrain, vegetation, search objects for a
  universal set of sweep width tables.
• Each locale needs sweep widths only for its area
  of responsibility, typical search objects, etc.
• Solution Develop a standard, practical, and
  scientifically based procedure for local
  resources to use when developing sweep width
  estimates.

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The Logan, West VirginiaDemonstration Project

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Project Support

• Sponsored by the U. S. National Search and Rescue
  Committee (NSARC)
• Funded by Department of Defense (NSARC member)
• Contract administered by U. S. Coast Guard (NSARC
  Chair) via the USCG Research and Development
  Center performed by Potomac Management Group
• Endorsed by NASAR and U. S. Air Force RCC
• Hosted by Logan Emergency Ambulance Service
  Authority

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Demonstration Project

• Principal Investigator R. Quincy Robe
• Location Chief Logan State Park, Logan, WV
• Host Roger Bryant, Director, Logan Emergency
  Ambulance Service Authority (LEASA)
• Participants Attendees at Logan SAR Weekend on
  15-16 June 2002
• Outstanding support and hospitality!

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Demonstration Project Objectives

• Design Practical Detection Experiment Procedures
  to determine Effective Sweep Width values for
  ground wilderness/rural searches.
• Supervise a Demonstration of the Procedures Using
  Ground SAR Personnel.
• Describe Method for Objectively Estimating POD
  from Effective Sweep Width, Effort, and Area.
• Report Results and Describe Future Work required
  to generalize their application.

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Concept of Operations (Preparation)

• Select a typical area and typical search object
  types (no more than 3 types)
• Select track(s) for searchers to follow (for at
  least 1 hourlonger is better)
• Choose date, select participants, make logistic
  arrangements, set up schedule
• Obtain/construct search objects ( 10 of each)

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Concept of Operations (Execution)

• Place objects at random locations along the track
  and random distances on either side
• Send searcher/data recorder pairs along the track
  at timed intervals (to ensure separation)
• Searchers move at normal search speed and report
  all sightings of search objects
• Data recorders record searcher sighting reports
  and other pertinent data
• Collect and analyze the recorded data

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Chief Logan State Park
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Select Search Track
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Search Objects
Orange Glove
Garbage Bag
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Determining Object Locations

• Useful range of distances off track
• Too close gt Insufficient data for longer ranges
• Too far gt Wasted detection opportunities
• Useful range of distances along track
• Too close gt Frequent reinforcement gt alertness
• Too far gt Track too long for reasonable time
• Use Average Maximum Detection Range

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Average Maximum Detection Range
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Select Object Placement

• Randomize
• Distances along the track
• Distances off track
• Right or Left of track
• Object types
• Determine locations based on largest AMDR
• Average separation along track of 3 ? AMDR
• Off track up to 1.5 ? AMDR

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Example of Object Locations(AMDR 100 m)
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Search Object Location Zones

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What is a Detection Opportunity?

• For the purposes of a detection experiment, a
  detection opportunity is defined as one complete
  pass by the search object.
• If there are 15 identical search objects of a
  given type and 30 searchers in an experiment,
  then there are a total of 15 x 30 450 detection
  opportunities for that type.
• Each detection opportunity has one of two
  results Detection or Non-detection.

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Important Notes

• When performing a detection experiment, it is
  important to understand that
• The relationship between the searcher (sensor)
  and the search object during the window of
  detection opportunity must be captured, and
• Knowing when non-detection occurs is just as
  important as knowing when detection occurs.

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Important Notes

• The experiment is NOT a competitive event
• The experiment does NOT measure individual
  searcher proficiency
• Do NOT tell searchers how many objects are
  present, how far off track, or give any other
  hints
• DO Collect additional data (e.g., weather, time
  of day, terrain and vegetation descriptions,
  searcher training/experience data, etc.) for
  later analysis

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Perform Experiment

• Secretly Place Objects at Selected Locations
• Send Searcher/Data Recorder Pairs along the
  Selected Track at Timed Intervals
• Collect Completed Detection Data Forms
• Remove Objects at Experiments Conclusion
• (Discard data for objects not found.)
• Compile, Sort and Analyze the Detection Data

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Detection Log
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Calculate Sweep Width

• Use the following property of sweep width
• The number of detections outside a swath one
  sweep width wide centered on the searchers track
  equals the number of missed detections inside
  that swath.
• Equivalently, the number of detections at lateral
  ranges greater than one-half the sweep width
  value are equal to the number of missed
  detections at lateral ranges less than one-half
  the sweep width value.

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Logan Demonstration Statistics

• 32 Searchers Participated
• 12 Orange Gloves were placed
• Glove AMDR 19 meters
• 32 x 12 384 Detection Opportunities
• 9 Black Garbage Bags were placed
• Bag AMDR 25 meters (1.5 x 25 37.5 meters)
• 32 x 9 288 Detection Opportunities

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Consolidated Detection Data
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Orange Glove Sweep Width
(AMDR 25 m) (12 Gloves, 32 Searchers)
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Orange Glove Half Lateral Range Curve
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Orange Glove Modified Sweep Width
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Orange Glove Modified Half LRC
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Black Bag Sweep Width
(AMDR 25 m) (9 Bags, 32 Searchers)
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Black Bag Lateral Range Curve
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Lessons Learned

• AMDR did not work well
• Poor choice of location?
• Poor technique?
• Should have been repeated several times in
  different locations
• May need to use maximum, rather than average
  maximum detection range
• Need steady flow of searcher/data recorder pairs

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Future Work

• Validate and refine detection experiment
  procedures in 3 different venues with different
  SAR groups and personnel during the next year.
• Publish the refined procedures and make them
  available upon request.
• Extend techniques to include aerial search over
  land (CAP, CASARA, etc).
• Develop more advanced search planning methods
  appropriate for the land SAR community.

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Future Work (continued)

• Develop functional requirements for software
  tools to support land SAR search planning.
• Survey existing software packages for synergistic
  opportunities.
• Develop software (modules) to support land SAR
  search planning functions.

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Conclusions

• A practical detection experiment procedure is
  feasible.
• Effective sweep width results make scientifically
  proven search planning methods available for use
  in land SAR.
• Objective, accurate, reliable POD estimation is
  possible
• More nearly optimal resource allocation can be
  done
• Increase probability of success (POS) at maximum
  rate.
• Minimize mean time to find survivors.
• Save more lives.
• Minimize risks to searchers through reduced
  exposure times.
• Minimize costs through shorter searches on
  average.

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Conclusions (continued)

• Effort needed is comparable to a SAREX.
• No special skills, tools or equipment required
  (although some items would be helpful).
• Data should be archived at a central site.
• Additional data gathered will support later
  analyses for important secondary effects
• For example, correction factors to extend
  usability of effective sweep width data to
  situations other than those of the experiments.

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THANK YOU!
Potomac Management Group, Inc. 510 King Street,
Suite 200 Alexandria, VA 22314 Attn J. R.
Frost 703-836-1037 or 202-267-6702 (USCG) E-mail
jfrost_at_potomacmgmt.com or
jfrost_at_comdt.uscg.mil