Joint One-Hop Flooding Communication and Event-Triggered Distributed Trajectory Fusion in Spatiotemporal Constraint Sensor Networks

In this work, we investigate the challenging problem of multitarget relay tracking with random appearance and disappearance in spatiotemporal sensor networks. In this scenario, sensor networks can be coordinated in a decentralized manner. In response, we propose a novel distributed trajectory fusion (DTF) method. Each local sensor, operating without prior knowledge of the network topology, performs different tasks based on target-oriented local trajectory estimation in three modes: source transmission mode (STM), passive receiving mode (PRM), and intermediate relay mode (IRM). Specifically, STM focuses on computing one-hop direct neighbors, PRM deals with the trajectory fusion, and IRM involves efficient communication through joint one-hop flooding and an event-triggered scheme, ensuring robust trajectory fusion. Our method incorporates Gaussian mixture approximated trajectory Poisson multi-Bernoulli filter with spatiotemporal constraints (GM-TPMB-STCs) to accurately capture the expected target movements as they enter the area of interest. Simulation results demonstrate the superiority of our proposed method over the existing distributed target state and density fusion methods in both accuracy and computational efficiency, offering a promising solution to multitarget tracking in complex environments.