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I read the page Trilateration algorithm for n amount of points and I have to do similar kind of trilateration in R.

I am not that expert in R, can anyone help, if there is similar function/package like mathematica in R?

  • Isn't any one aware of the mulilateration problem? How to solve in R – Hack_man Apr 20 '14 at 12:02
  • I am using R and I have a data set like > data_ref x y r 1 -1.90 -0.70 2.04 2 -0.90 -0.50 0.90 3 0.63 -0.65 0.72 4 0.31 -0.01 0.30 5 0.04 1.25 1.20 where x,y are the location of any antena, r is the distance of a device from there antenna. I am trying to minimize the residual as follows function(x,y,x0,y0,r) (sqrt(sum((x-x0)^2+(y-y0)^2))-r[1]+sqrt(sum((x-x0)^2+(y-y0)^2))-r) nlm(norm_vec,c(mean(data_ref$x),mean(data_ref$y)),x0=data_ref$x,y0=data_ref$y,r=data_ref$r) where x0, y0 is the seed point for iteration. but its not working, Can someone help me in understanding the flaw in my concept? Man – Hack_man Apr 20 '14 at 12:09
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As I didn't find the existing answers to this problem on StackExchange to be satisfying, I will add my own solution here. This uses geosphere package to calculate distance between two polar (latitude, longitude) coordinates.

For a data frame:

> head(coordinates)
        lat      lng distance
21 51.73832 10.72805     6000
31 51.76656 10.85404     6000
64 51.67559 10.82135     5000
70 51.75592 10.85369     5000
80 51.70379 10.79743     2000
89 51.68976 10.88211     6000

use

n <- nls( distance ~ distm(data.frame(lng, lat), c(lng_solution, lat_solution), fun=distHaversine),
          data = coordinates, start=list( lng_solution=10.9278778, lat_solution=51.6675738 ) )

Substitute the coordinates in the last line with your start-point estimate and make sure the unit of distance equals the unit of the dist-function (this is dependent on whatever dist-function you use, such as distHaversine, distRhumb, distMeeus, etc).

(Note that the geosphere package uses the uncommon convention of writing longitude before latitude.)

Using the destPoint function of geosphere we can plot the arcs of our measured radii

plot(coordinates[, c("lng", "lat")])
apply(coordinates[, c("lng", "lat", "distance")], 1, function (x) polygon(destPoint(c(x[1], x[2]), b=1:365, d=x[3])))

enter image description here

Use the following code to plot the confidence ellipse:

c <- confidenceEllipse(n, levels=0.95)
ellipse_line <- c[1, ]
ellipse_line <- rbind(ellipse_line, coef(n))
lines(ellipse_line)
text(x = mean(ellipse_line[, 1]), y = mean(ellipse_line[, 2]), 
     labels=format(distm(ellipse_line[1,], ellipse_line[2,]), nsmall=1))

enter image description here

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I used the following and it is working now.

norm_vec <- function(x) sqrt(sum((x[1]-data_ref$x)^2+(x[2]-data_ref$x)^2))-sum(data_ref$r)

nlm(norm_vec,c(mean(data_ref$x),mean(data_ref$y)))
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Thanks for sharing your code. Shouldn't it be:

norm_vec <- function(x) sqrt(sum((x[1]-data_ref$x)^2+(x[2]-data_ref$y)^2))-     sum(data_ref$r)
  • 1
    Adding some explanation to your code is a more acceptable answer. Try to add some explanation on why the other answer is incorrect and explain what the code does for other readers. – Branco Sep 21 '15 at 19:57

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