Trilateration using 3 latitude/longitude points, and 3 distances?

After some looking around at Wikipedia and the same question/answer at StackOverflow, I figured I would take a stab at it, and try to fill in the gaps.

First off, Not sure where you got the output, but it appears to be wrong. I plotted the points in ArcMap, buffered them to the distances specified, ran intersect to on the buffers, and then captured the vertex of intersection to get the solutions. Your proposed output is the point in green. I calculated the value in the callout box, which is about 3 meters of what ArcMap gave for solution derived from the intersect.

alt text

The math on the wikipedia page isn't too bad, just need to covert your geodetic coordinates to the cartesian ECEF, which can be found here. the a/x +h terms can be replaced by the authalic sphere radius, if you aren't using an ellipsoid.

Probably easiest just give you some well(?) documented code, so here it is in python

import math
import numpy

#assuming elevation = 0
earthR = 6371
LatA = 37.418436
LonA = -121.963477
DistA = 0.265710701754
LatB = 37.417243
LonB = -121.961889
DistB = 0.234592423446
LatC = 37.418692
LonC = -121.960194
DistC = 0.0548954278262

#using authalic sphere
#if using an ellipsoid this step is slightly different
#Convert geodetic Lat/Long to ECEF xyz
#   1. Convert Lat/Long to radians
#   2. Convert Lat/Long(radians) to ECEF
xA = earthR *(math.cos(math.radians(LatA)) * math.cos(math.radians(LonA)))
yA = earthR *(math.cos(math.radians(LatA)) * math.sin(math.radians(LonA)))
zA = earthR *(math.sin(math.radians(LatA)))

xB = earthR *(math.cos(math.radians(LatB)) * math.cos(math.radians(LonB)))
yB = earthR *(math.cos(math.radians(LatB)) * math.sin(math.radians(LonB)))
zB = earthR *(math.sin(math.radians(LatB)))

xC = earthR *(math.cos(math.radians(LatC)) * math.cos(math.radians(LonC)))
yC = earthR *(math.cos(math.radians(LatC)) * math.sin(math.radians(LonC)))
zC = earthR *(math.sin(math.radians(LatC)))

P1 = numpy.array([xA, yA, zA])
P2 = numpy.array([xB, yB, zB])
P3 = numpy.array([xC, yC, zC])

#from wikipedia
#transform to get circle 1 at origin
#transform to get circle 2 on x axis
ex = (P2 - P1)/(numpy.linalg.norm(P2 - P1))
i = numpy.dot(ex, P3 - P1)
ey = (P3 - P1 - i*ex)/(numpy.linalg.norm(P3 - P1 - i*ex))
ez = numpy.cross(ex,ey)
d = numpy.linalg.norm(P2 - P1)
j = numpy.dot(ey, P3 - P1)

#from wikipedia
#plug and chug using above values
x = (pow(DistA,2) - pow(DistB,2) + pow(d,2))/(2*d)
y = ((pow(DistA,2) - pow(DistC,2) + pow(i,2) + pow(j,2))/(2*j)) - ((i/j)*x)

# only one case shown here
z = numpy.sqrt(pow(DistA,2) - pow(x,2) - pow(y,2))

#triPt is an array with ECEF x,y,z of trilateration point
triPt = P1 + x*ex + y*ey + z*ez

#convert back to lat/long from ECEF
#convert to degrees
lat = math.degrees(math.asin(triPt[2] / earthR))
lon = math.degrees(math.atan2(triPt[1],triPt[0]))

print lat, lon

I'm not sure if I'm being naive, but, if you buffer each point by size, and then intersect all three circles that would get you the correct location?

You can compute the intersection using spatial APIs. Examples:

  • GeoScript
  • Java Topology Suite
  • NET Topology Suite
  • GEOS

The following notes use planarithmic geometry (i.e. you would have to project your coordinates into an appropriate local coordinate system).

My reasoning, with a worked example in Python, follows:

Take 2 of the data-points (call them a and b). Call our target point x. We already know the distances ax and bx. We can calculate the distance ab using Pythagoras' theorem.

>>> import math
>>> a = (1, 4)
>>> b = (3, 6)
>>> dist_ax = 3
>>> dist_bx = 5.385
# Pythagoras's theorem
>>> dist_ab = math.sqrt(abs(a[0]-b[0])**2 + abs(a[1]-b[1])**2)
>>> dist_ab
2.8284271247461903

Now, you can work out the angles of these lines:

>>> angle_abx = math.acos((dist_bx * dist_bx + dist_ab * dist_ab - dist_ax * dist_ax)/(2 * dist_bx * dist_ab))
>>> math.degrees(angle_abx)
23.202973815040256
>>> angle_bax = math.acos((dist_ax * dist_ax + dist_ab * dist_ab - dist_bx * dist_bx)/(2 * dist_ax * dist_ab))
>>> math.degrees(angle_bax)
134.9915256259537
>>> angle_axb = math.acos((dist_ax * dist_ax + dist_bx * dist_bx - dist_ab * dist_ab)/(2 * dist_ax * dist_bx))
>>> math.degrees(angle_axb)
21.805500559006095

Unfortunately I am short on time to complete the answer for you, However, now you know the angles, you can calculate two possible locations for x. Then, using the third point c you can calculate which location is correct.

Tags:

Algorithm

Distance

Geodesy

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