Multitarget Cooperative Motion Tracking Based on Quantum Belief Propagation

In this article, we introduce a novel cooperative target tracking algorithm, namely, the quantum-inspired belief propagation (BP), aimed at rectifying the limitations observed in existing localization algorithms employed in multitarget cooperative tracking scenarios. Leveraging the principles of quantum superposition, our algorithm seeks to alleviate the uncertainty inherent in message fusion within BP frameworks, thereby enhancing the accuracy and stability of multitarget cooperative localization. The utilization of the quantum Monte Carlo method facilitates the simulation of the message distribution process, with quantum particles embodying the superposition of multiple states concurrently. This approach effectively addresses the intractable integrations encountered in message updating on factor graphs (FGs), rendering the algorithm agnostic to the number of particles involved. Moreover, quantum unitary transformations and quantum closed box operations are deployed to encode FG function nodes for the propagation of quantum messages. This innovation surmounts the challenge posed by traditional FG function nodes’ inability to process quantum messages. Experimental findings corroborated the superiority of the proposed algorithm in terms of accuracy and robustness.