多智能体协同追踪移动目标，用共识算法提升精度与鲁棒性。
Decentralized Mobile Target Tracking Using Consensus-Based Estimation with Nearly-Constant-Velocity Modeling
- 基于共识的估计算法结合恒定速度模型，实现分布式协同追踪。
- 仿真显示均方估计误差随时间下降，定位更准更可靠。
- 适合通信受限、噪声大的复杂环境，如无人机群监控。
移动目标跟踪在监控和自主导航等场景中至关重要。本文提出一种去中心化的跟踪框架，采用基于共识的估计算法（CBEF）结合近似恒定速度（NCV）模型，预测运动目标状态。网络中的智能体通过共享局部观测信息，在通信受限和测量噪声条件下实现协同估计并达成共识。引入饱和滤波技术以增强对噪声数据的鲁棒性。仿真结果表明，所提方法能有效降低均方估计误差（MSEE），随时间持续改善估计精度与可靠性。研究证实CBEF在去中心化环境中的有效性，凸显其可扩展性及面对不确定性时的韧性。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。