An Efficient Implementation of the Generalized Labeled Multi-Bernoulli Filter

TL;DR

This paper introduces a streamlined, computationally efficient implementation of the GLMB filter by integrating prediction and update steps and utilizing Gibbs sampling for truncation, reducing complexity.

Contribution

It presents a novel combined prediction-update approach and a Gibbs sampling-based truncation algorithm, improving efficiency over previous separate-step methods.

Findings

Linear complexity in the number of measurements

Quadratic complexity in the number of objects

Enhanced computational efficiency

Abstract

This paper proposes an efficient implementation of the generalized labeled multi-Bernoulli (GLMB) filter by combining the prediction and update into a single step. In contrast to an earlier implementation that involves separate truncations in the prediction and update steps, the proposed implementation requires only one truncation procedure for each iteration. Furthermore, we propose an efficient algorithm for truncating the GLMB filtering density based on Gibbs sampling. The resulting implementation has a linear complexity in the number of measurements and quadratic in the number of hypothesized objects.