  Measurement-adaptive track initiation remains a critical design requirement
of many practical multi-target tracking systems. For labeled random finite sets
multi-object filters, prior work has been established to construct a labeled
multi-object birth density using measurements from multiple sensors. A
truncation procedure has also been provided that leverages a stochastic Gibbs
sampler to truncate the birth density for scalability. In this work, we
introduce a deterministic herded Gibbs sampling truncation solution for
efficient multi-sensor adaptive track initialization. Removing the stochastic
behavior of the track initialization procedure without impacting average
tracking performance enables a more robust tracking solution more suitable for
safety-critical applications. Simulation results for linear sensing scenarios
are provided to verify performance.
