Efficiency and sensitivity of methods for assessing ambiguity in data association decisions
Bret D. Kragel, Shawn M. Herman, Nick J. Roseveare · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2008
The central problem in multitarget, multisensor surveillance is that of determining which reports from separate sensors arise from common objects. Due to stochastic errors in the source reports, there may be multiple data association hypotheses with similar likelihoods. Moreover, established methods for performing data association make fundamental modeling assumptions that hold only approximately in practice. For these reasons, it is beneficial to include some measure of uncertainty, or ambiguity, when reporting association decisions. In this paper, we perform an analysis of the benefits versus runtime performance of three methods of producing ambiguity estimates for data association: enumeration of the k-best data association hypotheses, importance sampling, and Markov Chain Monte Carlo estimation. In addition, we briefly examine the sensitivity of ambiguity estimates to violations of the stochastic model used in the data association procedure.