{"title":"改进无线传感器网络的分布式移动地平线估计及其在移动机器人定位中的应用","authors":"Chaoyang Liang;Defeng He;Yun Chen","doi":"10.1109/JSEN.2025.3573283","DOIUrl":null,"url":null,"abstract":"This article investigates the distributed state estimation (DSE) problem for constrained nonlinear systems over wireless sensor networks (WSNs). We propose a novel hybrid distributed moving horizon estimation (DMHE) framework that combines both local measurements and prior estimates from neighboring nodes. The local estimation is performed in real time by solving an optimization problem that fuses these two information sources, thereby enhancing accuracy and robustness. A key advantage of our framework lies in designing the consensus prior weighting parameters offline via linear matrix inequalities (LMIs), providing a more flexible and scalable approach to improving global estimation performance. Furthermore, under disturbance boundedness, collective observability, and network connectivity, we prove that the global estimation error converges exponentially to a well-defined bound. The proposed framework generalizes two distinct DMHE approaches—consensus on prior estimates and consensus on measurements. Numerical results on a mobile robot localization task demonstrate its superior performance, underscoring both its theoretical soundness and practical feasibility.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 13","pages":"26032-26041"},"PeriodicalIF":4.3000,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Improving Distributed Moving Horizon Estimation Over Wireless Sensor Networks With Application to Mobile Robot Localization\",\"authors\":\"Chaoyang Liang;Defeng He;Yun Chen\",\"doi\":\"10.1109/JSEN.2025.3573283\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This article investigates the distributed state estimation (DSE) problem for constrained nonlinear systems over wireless sensor networks (WSNs). We propose a novel hybrid distributed moving horizon estimation (DMHE) framework that combines both local measurements and prior estimates from neighboring nodes. The local estimation is performed in real time by solving an optimization problem that fuses these two information sources, thereby enhancing accuracy and robustness. A key advantage of our framework lies in designing the consensus prior weighting parameters offline via linear matrix inequalities (LMIs), providing a more flexible and scalable approach to improving global estimation performance. Furthermore, under disturbance boundedness, collective observability, and network connectivity, we prove that the global estimation error converges exponentially to a well-defined bound. The proposed framework generalizes two distinct DMHE approaches—consensus on prior estimates and consensus on measurements. Numerical results on a mobile robot localization task demonstrate its superior performance, underscoring both its theoretical soundness and practical feasibility.\",\"PeriodicalId\":447,\"journal\":{\"name\":\"IEEE Sensors Journal\",\"volume\":\"25 13\",\"pages\":\"26032-26041\"},\"PeriodicalIF\":4.3000,\"publicationDate\":\"2025-06-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Sensors Journal\",\"FirstCategoryId\":\"103\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/11026774/\",\"RegionNum\":2,\"RegionCategory\":\"综合性期刊\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Sensors Journal","FirstCategoryId":"103","ListUrlMain":"https://ieeexplore.ieee.org/document/11026774/","RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Improving Distributed Moving Horizon Estimation Over Wireless Sensor Networks With Application to Mobile Robot Localization
This article investigates the distributed state estimation (DSE) problem for constrained nonlinear systems over wireless sensor networks (WSNs). We propose a novel hybrid distributed moving horizon estimation (DMHE) framework that combines both local measurements and prior estimates from neighboring nodes. The local estimation is performed in real time by solving an optimization problem that fuses these two information sources, thereby enhancing accuracy and robustness. A key advantage of our framework lies in designing the consensus prior weighting parameters offline via linear matrix inequalities (LMIs), providing a more flexible and scalable approach to improving global estimation performance. Furthermore, under disturbance boundedness, collective observability, and network connectivity, we prove that the global estimation error converges exponentially to a well-defined bound. The proposed framework generalizes two distinct DMHE approaches—consensus on prior estimates and consensus on measurements. Numerical results on a mobile robot localization task demonstrate its superior performance, underscoring both its theoretical soundness and practical feasibility.
期刊介绍:
The fields of interest of the IEEE Sensors Journal are the theory, design , fabrication, manufacturing and applications of devices for sensing and transducing physical, chemical and biological phenomena, with emphasis on the electronics and physics aspect of sensors and integrated sensors-actuators. IEEE Sensors Journal deals with the following:
-Sensor Phenomenology, Modelling, and Evaluation
-Sensor Materials, Processing, and Fabrication
-Chemical and Gas Sensors
-Microfluidics and Biosensors
-Optical Sensors
-Physical Sensors: Temperature, Mechanical, Magnetic, and others
-Acoustic and Ultrasonic Sensors
-Sensor Packaging
-Sensor Networks
-Sensor Applications
-Sensor Systems: Signals, Processing, and Interfaces
-Actuators and Sensor Power Systems
-Sensor Signal Processing for high precision and stability (amplification, filtering, linearization, modulation/demodulation) and under harsh conditions (EMC, radiation, humidity, temperature); energy consumption/harvesting
-Sensor Data Processing (soft computing with sensor data, e.g., pattern recognition, machine learning, evolutionary computation; sensor data fusion, processing of wave e.g., electromagnetic and acoustic; and non-wave, e.g., chemical, gravity, particle, thermal, radiative and non-radiative sensor data, detection, estimation and classification based on sensor data)
-Sensors in Industrial Practice