  In many multiobject tracking applications, including radar and sonar
tracking, after prefiltering the received signal, measurement data is typically
structured in cells. The cells, e.g., represent different range and bearing
values. However, conventional multiobject tracking methods use so-called point
measurements. Point measurements are provided by a preprocessing stage that
applies a threshold or detector and breaks up the cell's structure by
converting cell indexes into, e.g., range and bearing measurements. We here
propose a Bayesian multiobject tracking method that processes measurements that
have been thresholded but are still cell-structured. We first derive a
likelihood function that systematically incorporates an adjustable detection
threshold which makes it possible to control the number of cell measurements.
We then propose a Poisson Multi-Bernoulli (PMB) filter based on the likelihood
function for cell measurements. Furthermore, we establish a link to the
conventional point measurement model by deriving the likelihood function for
point measurements with amplitude information (AM) and discuss the PMB filter
that uses point measurements with AM. Our numerical results demonstrate the
advantages of the proposed method that relies on thresholded cell measurements
compared to the conventional multiobject tracking based on point measurements
with and without AM.
