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.

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Open Access

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