- #1

GlenLaRocca

- 28

- 11

I am new here and my response may have lost context info. This is a response to Dale.

As to whether it is a good idea. I considered the example of a ladder tilted out from the base of a building and picked a rather insane way to measure slope, that is with various noisy measurements of height at various distances. Would you use the known information of the ladder starting at the base and find the slope that minimized the weighted sum of squares? In this case, if you had one good measurement near the top and either very poor ones near the bottom or no measurements near the bottom, it would make quite a difference. But then I realized that the measurement near the top mattered more not just if it was accurate but because it is further from the base and gives more observability to the slope, given the constraint--a given lateral error gives a smaller angular error at a longer range. Obviously the tilted ladder is not a weighted LSF problem and yours probably isn't either.

So, like Dale said, don't do it.

As to whether it is a good idea. I considered the example of a ladder tilted out from the base of a building and picked a rather insane way to measure slope, that is with various noisy measurements of height at various distances. Would you use the known information of the ladder starting at the base and find the slope that minimized the weighted sum of squares? In this case, if you had one good measurement near the top and either very poor ones near the bottom or no measurements near the bottom, it would make quite a difference. But then I realized that the measurement near the top mattered more not just if it was accurate but because it is further from the base and gives more observability to the slope, given the constraint--a given lateral error gives a smaller angular error at a longer range. Obviously the tilted ladder is not a weighted LSF problem and yours probably isn't either.

So, like Dale said, don't do it.

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