In the summer of 2006, Rick Toman (Massachusetts State Police) and Dan O'Connor (NewSAR) organized a sweep width experiment and summit called "Detection in the Berkshires" at Mount Greylock in Massachusetts. In addition to the sweep-width experiment, Perkins & Roberts provided search tactics training for several teams, and the summit provided a chance for us to explore similarities and differences between formal search theory and formalized search tactics. It was an important the chance to meet many key people, compare notes, and discuss ideas. I wish I had been more diligent about following up. Many thanks to Rick & Dan for organizing the event, and to many others listed at the end. However, this post is mostly to provide a reference for the sweep width experiment.
When searching for an image for this post, I came across several works by E.B. Banning applying search theory to archaeology:
- Sweep widths and the detection of artifacts in archaeological survey. (2011) [Science Direct]
- Detection functions for archaeological survey (2006). [JSTOR]
- Archaeological Survey (2002 book). [Google books]
Now what would archaeologists be doing with sweep widths? Looking for nails, shards, and other small objects in the soil. What they nicely call "small scatters of generally unobtrusive artifacts on the surface".
In WiSAR we call them clues.
In the previous post, we began to build a theory of detection over time as the result of a very large number of independent glimpses. By assuming the environment to be fixed for awhile, we moved all the environmental factors into a constant (to be measured and tabulated), and simplified the function so it depended only on the range to the target.
In this post we simplify still further, introducing lateral range curves and the sweep width (also known as effective sweep width). We will follow Washburn's Search & Detection, Chapter 2. (So there's nothing new in this post. Just hopefully a clear and accessible presentation.)
We begin a four-part gentle introduction to search theory. Our topic is visual detection of targets by land searchers. Today we summarize Koopman Chapter 3, constructing the useful "inverse cube" detection model by starting from instantaneous glimpses with tiny detection probabilities.
- The Correlation
- Why Care?
- Why not just believe the data?
- What is the AMDR?
- Appendix Continue reading