Coast Guard Search & Rescue Metrics and Definitions Slides 2-6 Courtesy of: R.J. Koester, D.C. Cooper, J.R. Frost, R.Q. Robe Presented by Dave Larson.

Slides:



Advertisements
Similar presentations
S-Curves & the Zero Bug Bounce:
Advertisements

AREA & PERIMETER Created by Miss Mott. AREA What is area? Area is the amount of ____________ that an object takes up. Area is measured in _____________.
1 Exponential Distribution and Reliability Growth Models Kan Ch 8 Steve Chenoweth, RHIT Right: Wait – I always thought “exponential growth” was like this!
Using the Rule Normal Quantile Plots
POC, POD, POS Minnesota Wing Air Branch Director Course.
Potomac Management Group, Inc.1 Objective POD Estimation The Development of a Standard Method For Gathering and Using Detection Data R. Quincy Robe & Jack.
SEARCH PLAN VARIABLES CG Addendum Section H.5.
Observer Scanner Training by 1 st Lt. Alan Fenter.
Monday, June 01, 2015 ARRIVE: Algorithm for Robust Routing in Volatile Environments 1 NEST Retreat, Lake Tahoe, June
Paul James Doherty Park Ranger / GIS Specialist / Graduate Student.
The Search and Rescue Problem MARACOOS Fisheries Workshop 26 September 2011 Arthur Allen U.S. Coast Guard Office of Search and Rescue
Report on Intrusion Detection and Data Fusion By Ganesh Godavari.
What happens when the ship hits the fan. By Laura Harrison June 12 th, 2006 Geography 163.
The Capacity of Color Histogram Indexing Dong-Woei Lin NTUT CSIE.
Monte Carlo Simulation and Risk Analysis James F. Wright, Ph.D.
6-2 The Standard Normal Distribution
Norman W. Garrick Traffic Stream Flow Equations. Norman W. Garrick Basic Stream Flow Parameters Three types of parameters 1.Spacing and Concentration.
Probability Distributions Continuous Random Variables.
Forecasting Outside the Range of the Explanatory Variable: Chapter
Information Brokerage and Delivery to Mobile Sinks HyungJune Lee, Branislav Kusy, Martin Wicke.
Line of Best Fit. Age (months) Height (inches) Work with your group to make.
Parent Functions and Transformations
Coast Guard Research & Development Center Multi-Sensor Performance Prediction (MSPP) Tool-Set Kim Babcock U.S. Coast Guard R&D Center U.S. Coast Guard.
Slide Slide 1 Chapter 6 Normal Probability Distributions 6-1 Overview 6-2 The Standard Normal Distribution 6-3 Applications of Normal Distributions 6-4.
What We Know So Far… Data plots or graphs
Sensor Positioning in Wireless Ad-hoc Sensor Networks Using Multidimensional Scaling Xiang Ji and Hongyuan Zha Dept. of Computer Science and Engineering,
© The Catholic University of America Dept of Biomedical Engineering ENGR 104: Lecture 2 Statistical Analysis Using Matlab Lecturers: Dr. Binh Tran.
Section Copyright © 2014, 2012, 2010 Pearson Education, Inc. Lecture Slides Elementary Statistics Twelfth Edition and the Triola Statistics Series.
. Tracking Uncertainty in Search and Rescue Planning Art Allen U.S. Coast Guard Office of Search and Rescue
Software Reliability SEG3202 N. El Kadri.
Aim: How can the metric system help us make uniform measurements? Do Now: The smart board needs a new cover. What can we use to measure the smart board.
Chapter Two: Summarizing and Graphing Data 2.2: Frequency Distributions 2.3: ** Histograms **
Timothy Reeves: Presenter Marisa Orr, Sherrill Biggers Evaluation of the Holistic Method to Size a 3-D Wheel/Soil Model.
6-2: STANDARD NORMAL AND UNIFORM DISTRIBUTIONS. IMPORTANT CHANGE Last chapter, we dealt with discrete probability distributions. This chapter we will.
1 Measurement By Mrs. Pintor. 2 Essential Question How can we measure length and width?
Report on Intrusion Detection and Data Fusion By Ganesh Godavari.
1 Chapter 6 Continuous Probability Distributions.
4.6/4.7 Squares and Square Roots/Estimating Square Roots, p192/96 Warm Up Simplify = = = = = NS2.4 Use the inverse.
Aim: Curve Sketching Do Now: Worksheet Aim: Curve Sketching.
Metric System Book Definition In your own words… Picture A system of measurement used everywhere EXCEPT the United States that contains the units of meters,
Inference: Probabilities and Distributions Feb , 2012.
Statistical Surfaces Any geographic entity that can be thought of as containing a Z value for each X,Y location –topographic elevation being the most obvious.
L15 – Spatial Interpolation – Part 1 Chapter 12. INTERPOLATION Procedure to predict values of attributes at unsampled points Why? Can’t measure all locations:
Sampling Theory and Some Important Sampling Distributions.
Chapter 2 Data in Science. Section 1: Tools and Models.
Search Effort. The Balancing Act Limited Resources Limited Resources Planning Time in Search Area – how much time does it take to complete a search assignment?
4.6/4.7 Squares and Square Roots/Estimating Square Roots, p192/96 Warm Up Simplify = = = = = NS2.4 Use the inverse.
CHAPTER 5 CONTINUOUS PROBABILITY DISTRIBUTION Normal Distributions.
MOTION Motion: Change in position over time and is described by speed, velocity and acceleration.
Chapter 15: Exploratory data analysis: graphical summaries CIS 3033.
Evaluating Algebraic Expressions 4-7 Estimating Square Roots NS2.4 Use the inverse relationship between raising to a power and extracting the root of a.
Some Wildlife Census Techniques
SUR-2250 Error Theory.
Ranges of Magnitudes & Quantities
Civil Air Patrol – California Wing Visual Search Patterns and Procedures Mission Scanner Course Chapter 10 Version 1.2 (1 March 2014)
Mission Aircrew Course Search Planning and Coverage
Lecture Slides Elementary Statistics Twelfth Edition
Chapter 1 – The Nature of Science
Summary Presented by : Aishwarya Deep Shukla
Theory of Measurements and Errors
Line of Best Fit.
Use of the Visual Range of Detection to Estimate Effective Sweep Width for Land Search and Rescue Based On 10 Detection Experiments in North America 
Line of Best Fit.
Algebra 1 Section 6.6.
Matter and Measurement Vocabulary
Units of Measurement.
Line of Best Fit.
Standard Units of Measure
Standard Units of Measure
Using the Rule Normal Quantile Plots
Presentation transcript:

Coast Guard Search & Rescue Metrics and Definitions Slides 2-6 Courtesy of: R.J. Koester, D.C. Cooper, J.R. Frost, R.Q. Robe Presented by Dave Larson

Effective Sweep Width (Koopman) Cannot be measured directly Is an objective measure of detectability Large value => detection is easy Small value => detection is hard 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.)

A Uniform Random Distribution

Effective Sweep Width Number detected = 40. Number missed within sweep width = 0. Number detected outside sweep width = 0. Effective Sweep Width (Unrealistic Perfect Detection Making a Clean Sweep)

Effective Sweep Width Number detected = 40. Number missed within sweep width = 16. Number detected outside sweep width = 16. Effective Sweep Width Max Detection Range (More Typical Detection Pattern)

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” means “has the same effect as” Effective sweep width represents the expected amount of detection.

Distance to right or left of sensor at the closest point of approach (CPA) is Lateral Range Lateral range curve (Koopman) – Pd vs. Lateral Range

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

High-Altitude Visual Search LRC

Low-Altitude Visual Search LRC