Deterministic Multi-sensor Measurement-adaptive Birth using Labeled Random Finite Sets

TL;DR

This paper introduces a deterministic herded Gibbs sampling method for multi-sensor measurement-adaptive track initiation in multi-target tracking, enhancing robustness and efficiency for safety-critical applications.

Contribution

It proposes a deterministic herded Gibbs sampling approach to truncate the birth density, replacing stochastic methods for more reliable multi-sensor track initialization.

Findings

Improved robustness in track initialization.

Maintains average tracking performance.

Verified through simulations in linear sensing scenarios.

Abstract

Measurement-adaptive track initiation remains a critical design requirement of many practical multi-target tracking systems. For labeled random finite sets multi-object filters, prior work has been established to construct a labeled multi-object birth density using measurements from multiple sensors. A truncation procedure has also been provided that leverages a stochastic Gibbs sampler to truncate the birth density for scalability. In this work, we introduce a deterministic herded Gibbs sampling truncation solution for efficient multi-sensor adaptive track initialization. Removing the stochastic behavior of the track initialization procedure without impacting average tracking performance enables a more robust tracking solution more suitable for safety-critical applications. Simulation results for linear sensing scenarios are provided to verify performance.