Multi-Sensor Joint Adaptive Birth Sampler for Labeled Random Finite Set Tracking

This paper provides a scalable, multi-sensor measurement adaptive track initiation technique for labeled random finite set filters. A naive construction of the birth distribution leads to an exponential number newborn components in sensors. truncation criterion is established multi-Bernoulli density. The proposed shown have bounded L1 error generalized posterior used construct Gibbs sampler that produces truncated measurement-generated with quadratic complexity closed-form solution conditional sampling assuming linear Gaussian likelihoods provided, alongside approximate using Monte Carlo importance sampling. Multiple simulation results are provided verify efficacy criterion, as well reduction complexity.