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 multi-sensor measurement adaptive birth set leads to an exponential number of newborn components in the number of sensors. A truncation criterion is established for a multi-sensor measurement-generated labeled multi-Bernoulli random finite set that provably minimizes the L1-truncation error in the generalized labeled multi-Bernoulli posterior distribution. This criterion is used to construct a Gibbs sampler that produces a truncated measurement-generated labeled multi-Bernoulli birth distribution with quadratic complexity in the number of sensors. A closed form solution of the conditional sampling distribution assuming linear (or linearized) Gaussian likelihoods is provided, alongside an approximate solution using Monte Carlo importance sampling. Multiple simulation results are provided to verify the efficacy of the truncation criterion, as well as the reduction in complexity.