事件触发通信下的分布式非线性估计

牛梦飞; 朱炳旭; 蒋依流; 鞠爽

doi:10.12263/DZXB.20260347

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事件触发通信下的分布式非线性估计

更新时间：2026-06-15

- 事件触发通信下的分布式非线性估计
- Distributed Nonlinear Estimation Under Event-Triggered Communication
- 电子学报 2026年页码：1-11
- 作者机构：
  
  1.河南理工大学电气工程与自动化学院，河南焦作 454003
  2.河南省智能装备直驱技术与控制国际联合实验室，河南焦作 454003
  3.东南大学数学学院，江苏南京 211189
  4.石家庄铁道大学电气与电子工程学院，河北石家庄 050043
- 作者简介：
  
  牛梦飞男，1992年6月出生于河南省焦作市。2024年博士毕业于东南大学，获工学博士学位。现为河南理工大学电气工程与自动化学院讲师，硕士生导师。主要研究方向为群体系统分析、多传感系统滤波与融合等方面的研究工作，发表含IEEE TAC、Automatica长文在内的学术论文10余篇。 E-mail: mengfeiN@hpu.edu.cn
  朱炳旭男，2003年10月出生于河南省周口市。现为河南理工大学硕士研究生。主要研究方向为多数据融合，分布式安全估计。 E-mail: bingxuZ_2025@163.com
  蒋依流女，1994年8月出生于四川省资阳市。现为东南大学数学学院博士研究生。主要研究方向为多智能体系统、多智能体强化学习和分布式控制与决策。 E-mail: yiliujiang@seu.edu.cn
  鞠爽女，1992年4月出生于黑龙江省伊春市。2023年1月毕业于北京化工大学，获工学博士学位。现为石家庄铁道大学电气与电子工程学院讲师。主要研究方向为多智能体系统、多传感器融合感知、协同控制理论与应用等。 E-mail: jushuang@stdu.edu.cn
- 基金信息：
  
  国家自然科学基金(62503155;62303332);河南省科技攻关(252102210024);河南省高校基本科研业务费专项资金(NSFRF2502040);河北省自然科学基金(F2024210006)
- DOI：10.12263/DZXB.20260347
  中图分类号： TP391;
- 收稿：2026-04-08，
  
  录用：2026-04-30，
  
  网络首发：2026-06-15，
- 稿件说明：
移动端阅览
牛梦飞, 朱炳旭, 蒋依流, 等. 事件触发通信下的分布式非线性估计[J/OL]. 电子学报, 2026,1-11.

NIU Mengfei, ZHU Bingxu, JIANG Yiliu, et al. Distributed Nonlinear Estimation Under Event-Triggered Communication[J/OL]. ACTA ELECTRONICA SINICA, 2026, 1-11.
牛梦飞, 朱炳旭, 蒋依流, 等. 事件触发通信下的分布式非线性估计[J/OL]. 电子学报, 2026,1-11. DOI： 10.12263/DZXB.20260347.

NIU Mengfei, ZHU Bingxu, JIANG Yiliu, et al. Distributed Nonlinear Estimation Under Event-Triggered Communication[J/OL]. ACTA ELECTRONICA SINICA, 2026, 1-11. DOI： 10.12263/DZXB.20260347.

摘要

在基于无线传感网络的分布式估计架构中，传统周期性时间触发机制存在通信频次高、冗余数据交互量大等固有缺陷，极易导致网络通信资源过度消耗，继而影响分布式估计性能。鉴于此，本文提出一种随机事件触发通信机制，研究基于无线传感网络的事件触发分布式非线性估计方法。考虑实际传感观测系统普遍存在显著的强非线性特征，传统线性估计方法无法适配非线性系统状态演化规律，难以保证估计性能。为此，本文依托无迹卡尔曼滤波（Unscented Kalman Filtering， UKF）框架，利用无迹变换技术精准近似系统非线性状态传递过程，设计适配非线性观测模型的UKF型局部估计器有效改善局部估计性能。为有效降低邻居节点间持续高频信息交互带来的高昂通信成本，首先结合局部状态估计偏差特征，构建局部估计依赖的随机事件触发通信策略，并基于贝叶斯估计理论设计具备递推形式的事件触发局部估计器。其次，进一步利用协方差交叉融合技术，对交互的事件触发局部估计进行实时融合，设计得到事件触发分布式估计器，以确保节点在局部估计信息不完全交互的情况下分布式估计的一致性。再次，在网络全局可观意义下分析事件触发分布式估计器的收敛性，给出确保分布式估计方差有界的充分条件。最后，以机动目标跟踪场景进行仿真实验，验证所提算法在保障估计精度的同时能够显著降低网络通信负载。

Abstract

In the distributed estimation framework based on wireless sensor networks

the conventional periodic time-triggered mechanism suffers from inherent drawbacks such as high communication frequency and massive redundant data exchange

which easily leads to excessive consumption of network communication resources and further degrades the performance of distributed estimation. To address this issue

this paper proposes a stochastic event-triggered communication mechanism and investigates the event-triggered distributed nonlinear estimation method for wireless sensor networks. This paper focuses on the event-triggered distributed nonlinear filtering over wireless sensor networks by introducing a stochastic event-triggered communication scheme. Given the pronounced nonlinear characteristics of the system’s observation model

traditional linear estimation methods fail to match the state evolution rules of nonlinear systems and cannot guarantee satisfactory estimation performance. Thus

within the framework of unscented kalman filtering (UKF)

this paper adopts the unscented transformation to accurately approximate the nonlinear state propagation process

and develops a UKF-based local estimator tailored for nonlinear observation models to effectively improve local estimation performance. To reduce excessive estimation interactions caused by continuous and frequent information interaction among neighboring nodes

a local estimation-based stochastic event-triggered communication strategy is constructed according to the characteristics of local state estimation errors. Meanwhile

a recursive event-triggered local estimator is designed based on Bayesian estimation theory. By further employing the covariance intersection fusion technique

event-triggered local estimates are fused in real time to derive an event-triggered distributed estimator

which guarantees the consistency of distributed estimation under incomplete information exchange among neighboring nodes. Furthermore

the convergence of the proposed event-triggered distributed estimator is analyzed under the global observability of the network

and a sufficient condition is established to ensure the boundedness of the distributed covariance. Finally

simulation experiments are conducted using a typical maneuvering target tracking scenario to verify that the proposed algorithm can significantly reduce the network communication load while ensuring estimation accuracy.

关键词

Keywords

references

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