In my current problem, I am looking for an algorithm to reconstruct the position of multiple points in the 2D euclidean plane with incomplete distance information, $d_{ij}=||x_j-x_i||\in\text{dom}_a$. E.g. Information is:
- $x_i$: $\{d_{ii},d_{ij}, d_{ik}, d_{il}\}\in \text{dom}_a$
- $x_j$: $\{d_{ji}, d_{jj}, d_{jk}\} \in \text{dom}_a$, $\{d_{jl}\} \in \text{dom}_b$
- there are more than 2 domains and all domains are non overlapping.
This "binned distance" information is exact and is available of all points. I like to find the positions or rather areas with high likelihood, where the points can be found.
Currently I think, I have too less information to solve the nonlinear constrained optimization problem. On the other hand, I believe it should be enough to make a Bayesian approach work.
Information, I also have but currently don't make use of:
- the binned distance information are from time series and $\max(dx/dt)$ is known. This might be useful for finding the next solution of a given configuration in time.
Any suggestions how to obtain these regions of possible positions are appreciated.
To answer the questions I added a picture of the forward problem. Black dots represents all positions I like to reconstruct. For two points (green and blue) I exemplary plotted their bins (circle) for which I get the information.
E.g. For the two points close to the blue center, blue would return: $d_{\text{blue},1}$ and $d_{\text{blue},2}$ are within bin 2 where as $d_{\text{green},1}$ reports within bin 5 and $d_{\text{green},2}$ within bin 6. I do have the radius information for each bin.
All bins are a subset of the real line. Their union represents the real line. Their intersection is empty.
