Joint multi-object and clutter rate estimation with the single-cluster PHD filter
Isabel Schlangen, Vibhav Bharti, Emmanuel Delande, Daniel E. Clark · 2017
When working with real data, underlying parameters such as the detection or clutter rates are generally unknown and possibly varying over time, however the right parametrisation is crucial to extract proper statistics about the monitored objects. In this article, a single cluster Probability Hypothesis Density (PHD) filter is used to jointly estimate the location and number of a set of objects and the clutter rate over time. The algorithm is verified on a simulated scenario designed to emulate the challenging nature of Single-Molecule Localisation Microscopy (SMLM) imaging sequences and demonstrated on a similar scenario with real data.