Error when using lsqnonlin in for loop

2024 1 15
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更新时间：2024 1 15
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Hello, I am having some issuen when trying to implement the lsqnonlin iteration into a for loop.
I have the 3d coordinates of four known points, and also four known distances between each point to a fifth unknown point.
As I have a time-series of data I wanted to create a function that loops through all the data:
function Estimated_Position = trilaterate_new(p1, p2, p3, p4, distances, initial_guess)
% p1-p4 = 3D coordinated for the four known points, each the size of 100x3 (ex [x1 y1 z1; x2, y2, z2]
% distances = Known distance from each point to the fifth unknown point (ex. [d1 d2 d3 d4])
% initial_guess = initial guess of the position of point 5 (ex. [0 0 0])
options = optimoptions(@lsqnonlin,'Display','off');
for i = 1:100
objective_function = @(p5) [
sqrt((p1(i,1) - p5(1))^2 + (p1(i,2) - p5(2))^2 + (p1(i,3) - p5(3))^2) - distances(i,1);
sqrt((p2(i,1) - p5(1))^2 + (p2(i,2) - p5(2))^2 + (p2(i,3) - p5(3))^2) - distances(i,2);
sqrt((p3(i,1) - p5(1))^2 + (p3(i,2) - p5(2))^2 + (p3(i,3) - p5(3))^2) - distances(i,3);
sqrt((p4(i,1) - p5(1))^2 + (p4(i,2) - p5(2))^2 + (p4(i,3) - p5(3))^2) - distances(i,4)];
Estimated_Position(i,:) = lsqnonlin(objective_function, initial_guess, [], [], options);
end
end
The issue occurs when i=2 and I get down to execute lsqnonlin. I then receieve the error "Index in position 1 exceeds array bounds. Index must not exceed 1."
If anyone has any suguestions, please let me know

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Torsten
Torsten 2024-1-15
Execute the code
function Estimated_Position = trilaterate_new(p1, p2, p3, p4, distances, initial_guess)
% p1-p4 = 3D coordinated for the four known points, each the size of 100x3 (ex [x1 y1 z1; x2, y2, z2]
% distances = Known distance from each point to the fifth unknown point (ex. [d1 d2 d3 d4])
% initial_guess = initial guess of the position of point 5 (ex. [0 0 0])
size(p1)
size(p2)
size(p3)
size(p4)
size(distances)
size(initial_guess)
end
and tell us what MATLAB reports about the sizes.
Ellen Ryckenberg
Ellen Ryckenberg 2024-1-15
Hi! This is the size output:
ans =
100 3
ans =
100 3
ans =
100 3
ans =
100 3
ans =
1 4
ans =
1 3
dpb
dpb 2024-1-15
Ran in:
Ran in:
i=1;
N=10;
p1=rand(N,3); p2=rand(N,3); p3=rand(N,3); p4=rand(N,3); distances=rand(N,4);
initial_guess=ones(3,1);
for i=1:4
objective_function = @(p5) [
sqrt((p1(i,1) - p5(1))^2 + (p1(i,2) - p5(2))^2 + (p1(i,3) - p5(3))^2) - distances(i,1);
sqrt((p2(i,1) - p5(1))^2 + (p2(i,2) - p5(2))^2 + (p2(i,3) - p5(3))^2) - distances(i,2);
sqrt((p3(i,1) - p5(1))^2 + (p3(i,2) - p5(2))^2 + (p3(i,3) - p5(3))^2) - distances(i,3);
sqrt((p4(i,1) - p5(1))^2 + (p4(i,2) - p5(2))^2 + (p4(i,3) - p5(3))^2) - distances(i,4)];
Estimated_Position(i,:) = lsqnonlin(objective_function, initial_guess);
end
Local minimum found. Optimization completed because the size of the gradient is less than the value of the optimality tolerance. Local minimum possible. lsqnonlin stopped because the final change in the sum of squares relative to its initial value is less than the value of the function tolerance. Local minimum found. Optimization completed because the size of the gradient is less than the value of the optimality tolerance. Local minimum possible. lsqnonlin stopped because the final change in the sum of squares relative to its initial value is less than the value of the function tolerance.
Estimated_Position
Estimated_Position = 4×3
0.2834 -0.2140 0.3629 0.4751 0.1628 0.3481 0.8198 0.7758 0.6734 0.4578 0.5201 0.6442
Seems to work ok with made up data; something must be in what isn't being shown...
NOTA BENE: The array for Estimated_Position is not preallocated; perhaps your code sets an initial single vector?
clear Estimated_Position
Estimated_Position=zeros(1,3); % preset an initial vector
i=1;
N=10;
p1=rand(N,3); p2=rand(N,3); p3=rand(N,3); p4=rand(N,3); distances=rand(N,4);
initial_guess=ones(3,1);
for i=1:4
objective_function = @(p5) [
sqrt((p1(i,1) - p5(1))^2 + (p1(i,2) - p5(2))^2 + (p1(i,3) - p5(3))^2) - distances(i,1);
sqrt((p2(i,1) - p5(1))^2 + (p2(i,2) - p5(2))^2 + (p2(i,3) - p5(3))^2) - distances(i,2);
sqrt((p3(i,1) - p5(1))^2 + (p3(i,2) - p5(2))^2 + (p3(i,3) - p5(3))^2) - distances(i,3);
sqrt((p4(i,1) - p5(1))^2 + (p4(i,2) - p5(2))^2 + (p4(i,3) - p5(3))^2) - distances(i,4)];
Estimated_Position(i,:) = lsqnonlin(objective_function, initial_guess);
end
Local minimum found. Optimization completed because the size of the gradient is less than the value of the optimality tolerance. Local minimum possible. lsqnonlin stopped because the final change in the sum of squares relative to its initial value is less than the value of the function tolerance. Local minimum possible. lsqnonlin stopped because the final change in the sum of squares relative to its initial value is less than the value of the function tolerance. Local minimum possible. lsqnonlin stopped because the final change in the sum of squares relative to its initial value is less than the value of the function tolerance.
Estimated_Position
Estimated_Position = 4×3
0.6266 0.2776 0.3004 0.0396 0.6907 0.5794 0.2869 0.9305 0.8099 0.4261 0.5603 0.7756
Well, no, that didn't cause a bounds error, either.
We'll have to see the actual code that created the error, can't reproduce it from the snippet given, sorry...

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 采纳的回答

Torsten
Torsten 2024-1-15
编辑：Torsten 2024-1-15

1 个投票

As you can see, the dimension of "distances" is only 1x4. But it should be 100x4.
And better use
objective_function = @(p5) [
(p1(i,1) - p5(1))^2 + (p1(i,2) - p5(2))^2 + (p1(i,3) - p5(3))^2 - distances(i,1)^2;
(p2(i,1) - p5(1))^2 + (p2(i,2) - p5(2))^2 + (p2(i,3) - p5(3))^2 - distances(i,2)^2;
(p3(i,1) - p5(1))^2 + (p3(i,2) - p5(2))^2 + (p3(i,3) - p5(3))^2 - distances(i,3)^2;
(p4(i,1) - p5(1))^2 + (p4(i,2) - p5(2))^2 + (p4(i,3) - p5(3))^2 - distances(i,4)^2];
instead of
objective_function = @(p5) [
sqrt((p1(i,1) - p5(1))^2 + (p1(i,2) - p5(2))^2 + (p1(i,3) - p5(3))^2) - distances(i,1);
sqrt((p2(i,1) - p5(1))^2 + (p2(i,2) - p5(2))^2 + (p2(i,3) - p5(3))^2) - distances(i,2);
sqrt((p3(i,1) - p5(1))^2 + (p3(i,2) - p5(2))^2 + (p3(i,3) - p5(3))^2) - distances(i,3);
sqrt((p4(i,1) - p5(1))^2 + (p4(i,2) - p5(2))^2 + (p4(i,3) - p5(3))^2) - distances(i,4)];

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