Generalized Labeled Multi-Bernoulli Approximation of Multi-Object Densities

TL;DR

This paper introduces a new tractable approximation for multi-object densities that captures statistical dependence between objects, improving multi-object tracking accuracy in complex scenarios.

Contribution

It derives a Generalized Labeled Multi-Bernoulli (GLMB) density that matches key distribution features and minimizes divergence, enabling better multi-object inference.

Findings

The GLMB approximation effectively captures object dependencies.

The proposed method improves multi-object tracking in low SNR radar scenarios.

Simulation results demonstrate the approach's applicability and accuracy.

Abstract

In multi-object inference, the multi-object probability density captures the uncertainty in the number and the states of the objects as well as the statistical dependence between the objects. Exact computation of the multi-object density is generally intractable and tractable implementations usually require statistical independence assumptions between objects. In this paper we propose a tractable multi-object density approximation that can capture statistical dependence between objects. In particular, we derive a tractable Generalized Labeled Multi-Bernoulli (GLMB) density that matches the cardinality distribution and the first moment of the labeled multi-object distribution of interest. It is also shown that the proposed approximation minimizes the Kullback-Leibler divergence over a special tractable class of GLMB densities. Based on the proposed GLMB approximation we further demonstrate a tractable multi-object tracking algorithm for generic measurement models. Simulation results for a multi-object Track-Before-Detect example using radar measurements in low signal-to-noise ratio (SNR) scenarios verify the applicability of the proposed approach.