Measurement driven birth model for the generalized labeled multi-Bernoulli filter

TL;DR

This paper introduces a measurement driven birth model for the GLMB filter that adaptively generates target births from measurement data, removing the need for predefined birth distributions.

Contribution

The proposed MDB model for the GLMB filter adaptively generates target births based on measurements, enhancing flexibility and reducing reliance on prior knowledge.

Findings

Numerical results demonstrate improved tracking performance.

The MDB model effectively adapts to varying target birth scenarios.

Abstract

This paper presents a measurement driven birth (MDB) model for the generalized labeled multi-Bernoulli (GLMB) filter. The MDB model adaptively generates target births based on measurement data, thereby eliminating the dependence of \textit{a priori} knowledge of target birth distributions. Numerical results are provided to demonstrate the performance.