Mobile target tracking is crucial in various applications such as surveillance and autonomous navigation. This study presents a decentralized tracking framework utilizing a Consensus-Based Estimation Filter (CBEF) integrated with the Nearly-Constant-Velocity (NCV) model to predict a moving target's state. The framework facilitates agents in a network to collaboratively estimate the target's position by sharing local observations and achieving consensus despite communication constraints and measurement noise. A saturation-based filtering technique is employed to enhance robustness by mitigating the impact of noisy sensor data. Simulation results demonstrate that the proposed method effectively reduces the Mean Squared Estimation Error (MSEE) over time, indicating improved estimation accuracy and reliability. The findings underscore the effectiveness of the CBEF in decentralized environments, highlighting its scalability and resilience in the presence of uncertainties.

翻译：移动目标跟踪在监视与自主导航等众多应用场景中至关重要。本研究提出一种去中心化跟踪框架，该框架结合近匀速模型与共识估计滤波器，用于预测移动目标的状态。该框架使网络中的智能体能够通过共享局部观测信息并在存在通信约束与测量噪声的情况下达成共识，从而协同估计目标位置。研究采用基于饱和度的滤波技术以抑制噪声传感器数据的影响，从而增强系统的鲁棒性。仿真结果表明，所提方法能随时间推移有效降低均方估计误差，证明其提升了估计精度与可靠性。研究结果凸显了共识估计滤波器在去中心化环境中的有效性，并表明其在不确定性条件下具有良好的可扩展性与容错能力。