## 2 Related Works  
The Poisson multi-Bernoulli mixture (PMBM) conjugate priors for MTT were studied comprehensively by [PERSON] [11], considering the problems of both point target tracking and extended target tracking, and considering the problem of sets of objects to sets of trajectories. Some summarized studies from this thesis can be found in [6; 7; 8; 10; 12; 13; 14]. A derivation of the PMBM filter for multitarget tracking with the standard point target measurements without using probability generating functionals or functional derivatives was proposed in [12]. The conjugate prior of PMBM consisted of the union of a Poisson process and a multi-Bernoulli mixture (MBM), in which the MBM considered all the data association hypotheses; it could be implemented efficiently using a track-oriented multiple hypothesis tracking (MHT) formulation, while the Poisson part considered all targets that were never detected and enabled an efficient management of the number of hypotheses covering potential targets. A PMBM conjugate prior for multiple extended object filtering was proposed in [13], where a Poisson point process was used to describe the existence of yet undetected targets, while a multi-Bernoulli mixture described the distribution of the targets that were detected. The PMBM density was a conjugate prior for both the prediction and the update, which could preserve the PMBM form of the density, and a GGIW implementation was presented to approximate the unknown data associations. A new local hypothesis representation was presented in [14] to create a new Bernoulli component for each measurement, which considered computationally lighter alternatives to the extended object PMBM filter through two approximation methods. One was based on the track-oriented MB approximation, and the other was based on the variational MB approximation via KL divergence minimization. A novel consensus-based labeled MB (LMB) filter was proposed in [15] to tackle MTT in a communication resource-sensitive distributed sensor network (DSN). Two event-triggered strategies were proposed and incorporated into the consensus-based LMB: the integral-triggering strategy (ITS) and KL divergence. A PMBM filter was proposed in [5] for coexisting point and extended targets, which was updated for a generalized measurement model including measurements originated from point and extended targets. A single-target space accommodated both point and extended targets, and then derived the filtering recursion propagating Gaussian densities for point targets and GGIW densities for extended targets. Considering the above, the PMBM filter can be adopted in multitarget tracking filters, which can provide relatively optimal solutions for point targets or extended targets. A robust PMBM filter using adaptive birth distributions for the tracking of multiple targets was proposed in [16], which presented a novel measurement-driven adaptive birth distribution robust to the random locations where new targets are located. Beta distribution was employed to describe unknown detection probability to improve the method practicability. The detailed recursion and closed-form solutions were derived through two approximations: one approximating the target birth intensity and potential target intensity to the beta GGIW (BGGIW) mixture form, and the other approximating the existing Bernoulli component density to a single BGGIW form. The Gibbs sampler was adopted to resolve the problem of the computational bottleneck caused by data associations. A novel fusion framework for the Poisson MB (PMB) filter was proposed in [17], which integrated both the advantages of the TOMB/P filter in dealing with missed detection and the advantages of the MOMB/P filter in dealing with coalescence. The Bernoulli components in different MB distributions were associated with each other by KLD minimization to fuse the different PMB distributions.