  Many multi-object estimation problems require additional estimation of model
or sensor parameters that are either common to all objects or related to
unknown characterisation of one or more sensors. Important examples of these
include registration of multiple sensors, estimating clutter profiles, and
robot localisation. Often these parameters are estimated separately to the
multi-object estimation process, which can lead to systematic errors or
overconfidence in the estimates. These parameters can be estimated jointly with
the multi-object process based only on the sensor data using a single-cluster
point process model. This paper presents novel results for joint parameter
estimation and multi-object filtering based on a single-cluster second-order
Probability Hypothesis Density (PHD) and Cardinalised PHD (CPHD) filter.
Experiments provide a comparison between the discussed approaches using
different likelihood functions.
