Using Fisher Information Matrix Summary Statistics to Assess the Value of Collaborative Positioning Opportunities

In this paper, we consider the value of node collaboration for positioning. In particular, we look at the question of whether or not it is possible to assess the value of a collaborative opportunity using a low-complexity calculation. Using concepts from the optimal design of experiments, we consider the usage of Fisher information matrix summary statistics to provide these low-complexity calculations. These statistics are then evaluated for their abilities to help predict the post-collaboration availability and performance of a positioning solution, as well as their ability to aid in collaborator selection. The analysis is performed for both line-of-sight and non-line-of-sight environments. We show that the summary statistics are useful in assessing the value of collaborative positioning opportunities and discuss their strengths, weaknesses, and computational complexities.