Efficient Implementation of Multi-sensor Adaptive Birth Samplers for Labeled Random Finite Set Tracking

TL;DR

This paper introduces five efficiency enhancements for multi-sensor adaptive birth samplers in labeled RFS tracking, significantly reducing computational complexity with minimal impact on tracking accuracy.

Contribution

The paper presents novel algorithmic techniques that substantially improve the computational efficiency of multi-sensor adaptive birth procedures in labeled RFS tracking.

Findings

Efficiency enhancements reduce computational complexity.

Negligible impact on multi-target tracking performance.

Simulation results demonstrate practical benefits.

Abstract

Adaptive track initiation remains a crucial component of many modern 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 naive construction of this adaptive birth set density results in an exponential number of newborn components in the number of sensors. A truncation procedure was provided that leverages a Gibbs sampler to truncate the birth density, reducing the complexity to quadratic in the number of sensors. However, only a limited discussion has been provided on additional algorithmic techniques that can be employed to substantially reduce the complexity in practical tracking applications. In this paper, we propose five efficiency enhancements for the labeled random finite sets multi-sensor adaptive birth procedure. Simulation results are provided to demonstrate their computational benefits and show that they result in a negligible change to the multi-target tracking performance.