Enhancing Resolvable Group Target Tracking: Integration of Labeled MultiBernoulli Filter With Deep Learning Approaches

This article introduces an innovative approach for resolvable group target tracking (RGTT) through the integration of classical Bayesian filtering and sophisticated deep learning techniques. Numerous extant RGTT methods oversimplify target motion models and interactions within groups, limiting their applicability and compromising tracking performance. Deep learning methodologies are recurrently leveraged to overcome these challenges owing to their adeptness in delineating intricate mappings. We develop a neural network tailored for RGTT, incorporating a neural ordinary differential equation to articulate the motion model of targets. In addition, it predominantly integrates recurrent neural network (RNN) and graph neural network (GNN) modules. Similar to multitarget tracking tasks, the RNN module is enlisted here to model nonlinear dynamic systems. The distinction lies in the domain of resolvable group targets, where an additional reliance on graph theory becomes imperative for estimating group structures. Consequently, this article introduces a GNN module into the deep learning network to learn the interaction relationships among targets within the same group. Furthermore, the seamless fusion of this deep network with the labeled multiBernoulli filter augments the pragmatic viability of the deep learning algorithm. Simulation results elucidate the efficacy of the proposed algorithm.