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

TL;DR

This paper introduces a scalable multi-sensor measurement adaptive track initiation method for labeled RFS filters, reducing complexity via a novel truncation criterion and Gibbs sampling, verified through simulations.

Contribution

It proposes a truncation criterion for multi-sensor birth densities that bounds error and enables quadratic complexity Gibbs sampling for labeled RFS filters.

Findings

Truncation criterion effectively bounds L1 error in the posterior density.

Gibbs sampler achieves quadratic complexity in the number of sensors.

Simulation results confirm reduced complexity and maintained accuracy.

Abstract

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 distribution leads to an exponential number of newborn components in the number of sensors. A truncation criterion is established for a labeled multi-Bernoulli random finite set birth density. The proposed truncation criterion is shown to have a bounded L1 error in the generalized labeled multi-Bernoulli posterior density. 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 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.