PMBM Filter With Partially Grid-Based Birth Model With Applications in Sensor Management

This article introduces a Poisson multi-Bernoulli mixture (PMBM) filter in which the intensities of target birth and undetected targets are grid-based. A simplified version of the Rao-Blackwellized point mass filter is used to predict the intensity of undetected targets and to initialize tracks of targets detected for the first time. The grid approximation can efficiently represents intensities with abrupt changes with relatively few grid points compared to the number of Gaussian components needed in conventional PMBM implementations. This is beneficial in scenarios where the sensor’s field of view is limited. The proposed method is illustrated in a sensor management setting, where trajectories of sensors with limited fields of view are controlled to search for and track the targets in a region of interest.