The Adaptive Labeled Multi-Bernoulli Filter

TL;DR

This paper introduces the Adaptive Labeled Multi-Bernoulli filter, which enhances target tracking accuracy in critical scenarios while maintaining computational efficiency in noncritical situations by combining strengths of existing filters.

Contribution

It presents a novel adaptive filter that improves tracking precision during critical moments and reduces computational load during normal conditions, integrating features of Delta-G-LMB and LMB filters.

Findings

Improved target tracking accuracy in critical situations.

Reduced computational complexity in noncritical scenarios.

Combines advantages of existing labeled multi-Bernoulli filters.

Abstract

This paper proposes a new multi-Bernoulli filter called the Adaptive Labeled Multi-Bernoulli filter. It combines the relative strengths of the known Delta-Generalized Labeled Multi-Bernoulli and the Labeled Multi-Bernoulli filter. The proposed filter provides a more precise target tracking in critical situations, where the Labeled Multi-Bernoulli filter looses information through the approximation error in the update step. In noncritical situations it inherits the advantage of the Labeled Multi-Bernoulli filter to reduce the computational complexity by using the LMB approximation.