Dynamic Distributed Fusion Trajectory Tracking for Complex Maneuvering Targets

NIU Mu; ZHAO Dan; WANG Haoran; NIE Jing; ZHENG Dezhi

doi:10.12263/DZXB.20251186

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Dynamic Distributed Fusion Trajectory Tracking for Complex Maneuvering Targets

The Theory and Application of Swarm Intelligence Technology in the Information\-Rich Era | 更新时间：2026-06-16

- Dynamic Distributed Fusion Trajectory Tracking for Complex Maneuvering Targets
- ACTA ELECTRONICA SINICA Vol. 54, Issue 3, Pages: 970-980(2026)
- 作者机构：
  
  1.北京理工大学自动化学院，北京 100081
  2.东南大学系统科学系，江苏南京 211189
  3.北京理工大学前沿交叉科学院，北京 100081
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(62306071;U22B2046)
- DOI：10.12263/DZXB.20251186
  CLC： TP212;TP273
- Received：05 January 2026，
  
  Accepted：23 January 2026，
  
  Published：25 March 2026
- 稿件说明：
移动端阅览
牛牧, 赵丹, 王昊冉, 等. 面向复杂机动目标的动态分布式融合轨迹跟踪[J]. 电子学报, 2026, 54(03): 970-980.

NIU Mu, ZHAO Dan, WANG Haoran, et al. Dynamic Distributed Fusion Trajectory Tracking for Complex Maneuvering Targets[J]. Acta Electronica Sinica, 2026, 54(03): 970-980.
牛牧, 赵丹, 王昊冉, 等. 面向复杂机动目标的动态分布式融合轨迹跟踪[J]. 电子学报, 2026, 54(03): 970-980. DOI：10.12263/DZXB.20251186

NIU Mu, ZHAO Dan, WANG Haoran, et al. Dynamic Distributed Fusion Trajectory Tracking for Complex Maneuvering Targets[J]. Acta Electronica Sinica, 2026, 54(03): 970-980. DOI：10.12263/DZXB.20251186

摘要

本文针对资源受限的分布式多传感器系统中的状态估计问题，探讨了在有限通信带宽与计算能力下估计精度与系统开销之间的平衡问题。传统多传感器融合架构通常采用预设静态算法与固定网络拓扑，缺乏对环境动态、任务需求的适应能力，难以在长期运行中维持最优的性能与效率权衡。为此，本文提出了一种基于事件触发的自适应融合调度框架，通过智能的事件驱动机制，动态组织传感器资源，从而在保证估计精度的同时，显著降低不必要的通信与计算消耗。面向事件的动态融合单元使系统依据实时判定的事件，动态触发传感器节点的智能分群与重组。这些节点基于特定的任务，临时组建为面向当前事件的融合子单元。该机制使得系统能够聚焦关键信息，避免了对所有节点进行持续全局融合所带来的巨大开销。针对事件的判定，本文重点研究并比较了两种设计策略：其一是基于局部新息的事件设计，即节点仅根据自身测量与新息形成触发事件；其二是基于全局反馈的事件设计，即融合估计值从融合中心反馈至各节点进行判断。前者完全分布式，私密性强；后者则具备更全面的系统视角，有利于做出更优的集群决策。其次，在临时融合单元形成并完成局部融合后，本文引入了混合融合机制对局部结果进行二次融合。该机制在最终输出层面进一步实现了估计精度与融合成本的平衡。一系列仿真实验表明，基于局部新息和基于全局反馈的两种事件设计方案均能成功地将融合算法的选择问题从静态配置转化为动态的实时调度。系统能够依据实时任务需求与系统资源状况，自主调整传感器分群，从而在动态变化的环境中持续保持精度与效率的最佳折中。此外，对比实验进一步揭示，在一定调度参数下，基于全局反馈的事件设计因其拥有全局系统状态信息，能够做出更优的调度决策，从而在估计性能上优于完全分布式的局部新息方案，但这是以引入周期性全局通信为代价的。本文提出的基于事件的自适应融合调度框架，为资源受限的分布式感知系统提供了一种灵活、高效的解决方案。它通过事件实现对于传感器的动态调度，有效解决了复杂机动目标的轨迹跟踪问题中估计精度与硬件开销的平衡问题。

Abstract

This paper addresses the state estimation problem in resource-constrained distributed multi-sensor systems

focusing on the trade-off between estimation accuracy and system overhead under limited communication bandwidth and computational capabilities. Traditional multi-sensor fusion architectures often rely on predetermined static algorithms and fixed network topologies

which lack adaptability to dynamic environmental changes and task requirements

making it difficult to maintain an optimal balance between performance and efficiency over prolonged operation. To overcome these limitations

this study proposes an event-triggered adaptive fusion scheduling framework. By employing an intelligent event-driven mechanism

the framework dynamically organizes sensor resources

thereby ensuring estimation accuracy while significantly reducing unnecessary communication and computational costs. The event-oriented dynamic fusion unit enables the system to intelligently cluster and reconfigure sensor nodes in real time based on detected events. These nodes are temporarily grouped into task-specific fusion sub-units tailored to the current event. This mechanism allows the system to focus on critical information

avoiding the substantial overhead associated with continuous global fusion across all nodes. For event determination

this paper investigates and compares two design strategies: one based on local innovation

where each node generates triggering events solely from its own measurements and innovation sequence; and the other based on global feedback

where the fused estimate is fed back from the fusion center to each node for decision-making. The former is fully distributed and offers stronger privacy

while the latter provides a more comprehensive system perspective

facilitating better clustering decisions. Furthermore

after the temporary fusion unit is formed and performs local fusion

a hybrid fusion mechanism is introduced to conduct secondary fusion of the local results. This mechanism achieves an additional balance between estimation accuracy and fusion cost at the final output stage. A series of simulation experiments demonstrate that both event design schemes—based on local innovation and global feedback: successfully transform the fusion algorithm selection from a static configuration into a dynamic real-time scheduling process. The system can autonomously adjust sensor clustering according to real-time task demands and resource availability

thereby sustaining an optimal trade-off between accuracy and efficiency in dynamically changing environments. Comparative experiments further reveal that

under certain scheduling parameters

the global feedback-based design outperforms the fully distributed local innovation approach in estimation performance due to its access to global system state information

which enables superior scheduling decisions. This advantage

however

comes at the cost of introducing periodic global communication. The proposed event-triggered adaptive fusion scheduling framework offers a flexible and efficient solution for resource-constrained distributed sensing systems. By dynamically scheduling sensors via event-driven mechanisms

it effectively resolves the balance between estimation accuracy and hardware overhead in challenging applications such as trajectory tracking of highly maneuverable targets.

关键词

Keywords

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