Robust Multi-Sensor Multi-Target Tracking Using Possibility Labeled Multi-Bernoulli Filter

TL;DR

The paper addresses multi-sensor multi-target tracking under epistemic uncertainty by introducing a Possibility Labeled Multi-Bernoulli (LMB) filter grounded in Outer Probability Measures (OPMs). It develops closed-form LMB prediction, update, and joint prediction-update rules, along with a rigorous fusion framework for LMB UFS across sensors, including a label-matching strategy for heterogeneous label spaces. The method is extended to centralized and distributed MSMTT architectures and validated via simulations that compare against probabilistic baselines, showing superior robustness under limited information. The work advances robust and scalable MSMTT in scenarios with incomplete information, enabling more reliable perception in autonomous sensing systems.

Abstract

With the increasing complexity of multiple target tracking scenes, a single sensor may not be able to effectively monitor a large number of targets. Therefore, it is imperative to extend the single-sensor technique to Multi-Sensor Multi-Target Tracking (MSMTT) for enhanced functionality. Typical MSMTT methods presume complete randomness of all uncertain components, and therefore effective solutions such as the random finite set filter and covariance intersection method have been derived to conduct the MSMTT task. However, the presence of epistemic uncertainty, arising from incomplete information, is often disregarded within the context of MSMTT. This paper develops an innovative possibility Labeled Multi-Bernoulli (LMB) Filter based on the labeled Uncertain Finite Set (UFS) theory. The LMB filter inherits the high robustness of the possibility generalized labeled multi-Bernoulli filter with simplified computational complexity. The fusion of LMB UFSs is derived and adapted to develop a robust MSMTT scheme. Simulation results corroborate the superior performance exhibited by the proposed approach in comparison to typical probabilistic methods.

Figures (6)

• Figure 1: The cost matrix of association
• Figure 2: Sensor network
• Figure 3: OSPA$^{(2)}$ position distance of case A
• Figure 4: Cardinality estimation of case A
• Figure 5: OSPA$^{(2)}$ position distance of case B

Theorems & Definitions (6)

• Proposition 1
• Proposition 2
• Proposition 3
• proof : Proof of proposition \ref{['prop_predict']}
• proof : Proof of proposition \ref{['prop_update']}
• proof : Proof of proposition \ref{['prop_fusion']}