reverse 3D euclidean distance
显示 更早的评论
Hi there,
I am standing at an unknown point U(x,y,z) in the room. I can measure 3 (euclidean) distances D to 3 known points P in the room. I try to find the point, where I am at. My equation system looks like this:
(x-3)²+(y-1)²+(z-4)²=D1²=81
(x-12)²+(y-1)²+(z-4)²=D2²=36
(x-34)²+(y-2)²+(z-4)²=D3²=601
I can put the known points into a matrix P, the measured distance in a vector D:
P=[3 1 4;12 1 4; 34 2 4]
P =
3 1 4
12 1 4
34 2 4
D=[81 36 601]
Do you know how I can find U(x,y,z) ?
I am not sure if I can use
D = pdist(X,'euclidean');
采纳的回答
Star Strider
2019-7-25
Try this:
P=[3 1 4;12 1 4; 34 2 4];
D=[81 36 601];
fcn = @(b,x) (b(1)-x(:,1)).^2 + (b(2)-x(:,2)).^2 + (b(3)-x(:,3)).^2;
B = fminsearch(@(b) norm(D(:) - fcn(b,P)), [1; 1; 1])
producing:
B =
10.0000
5.0000
-0.0000
that are the (x,y,z) coordinates, as best fminsearch can calculate them.
2 个评论
Ferdinand Grosse-Dunker
2019-7-25
Star Strider
2019-7-25
As always, my pleasure.
The documentation for the fminsearch function is at the link. It is an unconstrained optimiser that uses a derivative-free method to find the minimum.
The code I use for my objective function ‘fcn’ and as an argument to fminsearch are Anonymous Functions. They are quite useful for coding short functions, although they have their limitations.
I use the norm function so that the fminsearch function finds the minimum value that satisfies the sum-of-squares criterion (since this is essentially a curve-fiting problem).
更多回答（1 个）
Akira Agata
2019-7-25
There should be 2 answers.
Here is my try.
P = [3 1 4;12 1 4; 34 2 4];
D = [81 36 601];
func = @(x) (vecnorm(x - P(1,:))-sqrt(D(1)))^2+...
(vecnorm(x - P(2,:))-sqrt(D(2)))^2+...
(vecnorm(x - P(3,:))-sqrt(D(3)))^2;
x1 = fminsearch(func,[1 1 1]);
x2 = fminsearch(func,[10 10 10]);
>> x0
x0 =
10.0000 5.0000 -0.0000
>> x1
x1 =
10.0000 5.0000 8.0000
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