Tracking correlated, simultaneously evolving target populations
Ronald Mahler · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2016
Multisensor-multitarget tracking algorithms are typically based on numerous statistical independence assumptions. This paper is the fifth in a series aimed at weakening such assumptions. It addresses the statistics of correlated, simultaneously evolving multitarget populations. The correlation between two multitarget popula-tions is approximately modeled using bivariate i.i.d.c. (independent, identically distributed cluster) distributions. Based on this, a joint tracking filter for such populations is devised, in analogy with the cardinalized probability hypothesis density (CPHD) filter.