Using a High-Fidelity Simulation Framework for Performance Singularity Identification and Testing
Christopher J. Scrapper Jr, Rajmohan Madhavan, Stephen B. Balakirsky
A common way to evaluate the performance of a system is to compare the algorithmic outputs with ground truth to identify divergences in the system's performance and discover the errors it is prone to. In the absence of such ground truth or as a follow-on to performance evaluation, performance analysis at the algorithmic level can provide developers insight into performance singularities. Such performance singularity identification and testing provides real-time meta-data that allows developers to understand the impact of singularities on the overall performance of the system. As an example of the concepts developed in this paper, we present a navigation solution based on image registration algorithms and the methodology used for the identification and testing of performance singularities of this algorithm.
Proceedings of Applied Imagery Pattern Recognition (AIPR) 2007
, Madhavan, R.
and Balakirsky, S.
Using a High-Fidelity Simulation Framework for Performance Singularity Identification and Testing, Proceedings of Applied Imagery Pattern Recognition (AIPR) 2007, Washington, DC, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=823043
(Accessed November 30, 2023)