Hybrid Liquid Neural Network-Random Finite Set Filtering for Robust Maneuvering Object Tracking

TL;DR

This paper introduces a hybrid liquid neural network and random finite set filtering approach that learns object dynamics directly from data, significantly improving the robustness and accuracy of maneuvering object tracking in cluttered environments.

Contribution

It presents a novel integration of liquid neural networks into the RFS framework, enabling adaptive, data-driven modeling of complex object motion for the first time.

Findings

Enhanced tracking accuracy in complex scenarios

Robustness to nonlinear and maneuvering motions

Effective handling of cluttered environments

Abstract

This work addresses the problem of tracking maneuvering objects with complex motion patterns, a task in which conventional methods often struggle due to their reliance on predefined motion models. We integrate a data-driven liquid neural network (LNN) into the random finite set (RFS) framework, leading to two LNN-RFS filters. By learning continuous-time dynamics directly from data, the LNN enables the filters to adapt to complex, nonlinear motion and achieve accurate tracking of highly maneuvering objects in clutter. This hybrid approach preserves the inherent multi-object tracking strengths of the RFS framework while improving flexibility and robustness. Simulation results on challenging maneuvering scenarios demonstrate substantial gains of the proposed hybrid approach in tracking accuracy.