# NavList:

## A Community Devoted to the Preservation and Practice of Celestial Navigation and Other Methods of Traditional Wayfinding

**Re: Still on LOP's**

**From:**Chuck Taylor

**Date:**2002 Apr 20, 21:36 US/PACIFIC

I propose that a computer simulation might help settle this argument as to the probablity that one's actually position lies within the "cocked hat". I am hoping that someone has the time and inclination to carry out the simulation. The simulation would proceed as follows: 1. On a Cartesian grid (like ordinary graph paper with the x- and y-units equal in length), pick three points to be the "landmarks", and a point between them to be our "true position". (If this grid bothers you, pretend that you are near the equator. A square grid makes the computations easier without loss of generality.) 2. Select the scale as desired, but 2 to 4 miles between the true position and the landmarks might be realistic. 3. Construct the equations of the lines from each landmark to the true position. Compute the true bearing for each in degrees. 4. Repeat the following 1000 times: a. For each bearing line, generate a random number from a Normal distribution with mean 0 and standard deviation 1. (This is the distribution associated with the classic bell-shaped curve; about half the time this number will be negative and about half the time positive.) Treat these numbers as the bearing errors in degrees. Add these numbers to the bearings. These will give the simulated bearings for one iteration. With this distribution most of the simulated bearings will be within about +/- 3 degrees of the actual bearing, which seems fairly reasonable for a good hand-bearing compass. b. Using the simulated bearings, compute the equations of simulated bearing lines from each landmark. c. Compute the pairwise intesections of the three simulated bearing lines. Thise will be the vertices of the triangle (the "cocked hat"). d. Determine analytically whether or not the true position lies within the triangle. If yes, tally one for the "yes" column. If no, tally one for the "no" column. 5. When 1000 iterations have been completed, the number in the "yes" column divided by 1000 will be our estimate of the percentage of the time we can expect our actual position to lie within the cocked hat. The key idea here is that the randomness is in the bearing, and we need a way to translate that into the desired probablilty. I suppose one could do a triple integral to solve the problem analytically, but a simulation sounds more appealing to me. Any takers? Chuck Taylor Everett, WA, USA