Date of Award
5-12-2024
Degree Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Electrical Engineering and Computer Science
Advisor(s)
Pramod Varshney
Keywords
Gibbs Sampling;Measurement Adaptive Birth;Random Finite Sets;State Estimation;Target Tracking
Subject Categories
Computer Sciences | Physical Sciences and Mathematics
Abstract
This dissertation provides a scalable, multi-sensor measurement adaptive track initiation technique for labeled random finite set filters. The lack of a well-defined, systematic approach is problematic for many applications, especially when fusing ambiguous sensor measurements. We begin by showing that a naive solution leads to an exponential number of newborn components in the number of sensors. An efficient solution is derived by formulating a ranked assignment truncation problem. A truncation criterion is established for a labeled multi-Bernoulli random finite set birth density that has a bounded L1 error in the generalized labeled multi-Bernoulli posterior density. This criterion is used to construct stochastic and deterministic Gibbs samplers that produce a truncated measurement-generated labeled multi-Bernoulli birth distribution with quadratic complexity in the number of sensors. An efficient approach for Gibbs sample generation is provided and two early termination criteria are proposed. A closed-form solution of the conditional sampling distribution assuming linear Gaussian likelihoods is provided, alongside an approximate solution using Monte Carlo importance sampling for invertible and non-invertible measurement functions. Multiple simulation results are provided to verify the efficacy as well as the reduction in complexity.
Access
Open Access
Recommended Citation
Trezza, Anthony, "On Multi-sensor Adaptive Birth Theory for Labeled Random Finite Sets Tracking" (2024). Dissertations - ALL. 1891.
https://surface.syr.edu/etd/1891