Multitarget tracking using sensors with known correlations
MetadataShow full item record
© 2016 SPIE. This paper is the fourth in a series aimed at weakening the independence assumptions that are typically presumed in multitarget tracking. Specifically, we assume that, in a multisensory scenario, the sensors are not necessarily independent but, rather, have known correlations (i.e., their joint single-target joint likelihood function is known). From this, we construct a multitarget measurement model for sensors with known correlations. From this model we derive, as an illustrative example, the filtering equations for a probability hypothesis density (PHD) filter for sensors with known correlations. We emphasize the two-sensor case of this filter, for which the measurement-update equations involve a summation over all measurement-to-measurement associations between the two sensors.
Showing items related by title, author, creator and subject.
Kühnapfel, Thorsten (2009)For humans, hearing is the second most important sense, after sight. Therefore, acoustic information greatly contributes to observing and analysing an area of interest. For this reason combining audio and video cues for ...
McAtee, Brendon Kynnie (2003)Remote sensing of land surface temperature (LST) is a complex task. From a satellite-based perspective the radiative properties of the land surface and the atmosphere are inextricably linked. Knowledge of both is required ...
Shah, Milin ; Agrawal, Vaibhav; Shinde, Yogesh; Utikar, Ranjeet; Pareek, Vishnu (2017)Gas-solid bubbling fluidized bed (BFB) is widely applied in industrial processes such as combustion, polymerization, cracking, etc. In BFB, gas-solid flow depends on formation of bubbles and its characteristics such as ...