Sensor Control for Multi-Object Tracking Using Labeled Multi-Bernoulli Filter

TL;DR

This paper introduces a sensor-control approach for multi-target tracking using the labeled multi-Bernoulli filter, optimizing sensor actions based on combined state and cardinality estimation errors, demonstrated through simulations.

Contribution

It proposes a novel sensor-control method that integrates a task-driven cost function with the LMB filter for improved multi-target tracking.

Findings

Successfully guides a mobile sensor in complex scenarios

Effectively balances state and cardinality estimation errors

Enhances multi-target tracking accuracy

Abstract

The recently developed labeled multi-Bernoulli (LMB) filter uses better approximations in its update step, compared to the unlabeled multi-Bernoulli filters, and more importantly, it provides us with not only the estimates for the number of targets and their states, but also with labels for existing tracks. This paper presents a novel sensor-control method to be used for optimal multi-target tracking within the LMB filter. The proposed method uses a task-driven cost function in which both the state estimation errors and cardinality estimation errors are taken into consideration. Simulation results demonstrate that the proposed method can successfully guide a mobile sensor in a challenging multi-target tracking scenario.