Relative multiple space object tracking using intensity filters

Multitarget intensity filters, such as the probability hypothesis density (PHD) filter and cardinalized probability hypothesis density (CPHD) filter have been recently proposed as a means to track multiple space objects from both ground-based and space-based platforms. In many applications, the CPHD is chosen over the PHD filter, as it has been claimed to offer significant improvements in the accuracy of both its cardinality estimates and state estimates. To that end, in this study, Gaussian mixture implementations of both the PHD and CPHD filters are developed to track the relative states of nearby space objects with respect to an inspector spacecraft using angles-only measurements. The performance of each solution is evaluated over several metrics, including cardinality error, optimal sub-pattern assignment distance, and execution speed.