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17 May 2016Tracking correlated, simultaneously evolving target populations
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.
Ronald Mahler
"Tracking correlated, simultaneously evolving target populations", Proc. SPIE 9842, Signal Processing, Sensor/Information Fusion, and Target Recognition XXV, 98420C (17 May 2016); https://doi.org/10.1117/12.2224640
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Ronald Mahler, "Tracking correlated, simultaneously evolving target populations," Proc. SPIE 9842, Signal Processing, Sensor/Information Fusion, and Target Recognition XXV, 98420C (17 May 2016); https://doi.org/10.1117/12.2224640