{"title":"具有重尾噪声和缺失测量的多速率多传感器非线性动态系统的序列融合。","authors":"Guiting Hu , Zhengjiang Zhang , Luping Xu","doi":"10.1016/j.isatra.2024.11.005","DOIUrl":null,"url":null,"abstract":"<div><div>This paper focuses on sequential fusion estimation for multi-rate multi-sensor nonlinear dynamic systems with heavy-tailed noise and missing measurements. On the basis of Bayesian inference, a sequential Student’s <em>t</em>-based unscented Kalman filter (SSTUKF), together with its square-root form (SR-SSTUKF), is proposed by using the unscented transform to calculate Student’s <em>t</em> weighted integrals. Considering the nonstationary measurement noise and/or accumulated computation error, adaptive factors are introduced by the <em>t</em>-test to suppress uncertainties. Additionally, the complexity computation and convergence analysis of the SR-SSTUKF are presented. The validity and robustness of the proposed sequential fusion method are illustrated by an example of agile target tracking. Simulation results indicate that the SR-SSTUKF with adaptive factors can further enhance accuracy and yield reliable estimations.</div></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":"156 ","pages":"Pages 344-364"},"PeriodicalIF":6.3000,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Sequential fusion for multi-rate multi-sensor nonlinear dynamic systems with heavy-tailed noise and missing measurements\",\"authors\":\"Guiting Hu , Zhengjiang Zhang , Luping Xu\",\"doi\":\"10.1016/j.isatra.2024.11.005\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>This paper focuses on sequential fusion estimation for multi-rate multi-sensor nonlinear dynamic systems with heavy-tailed noise and missing measurements. On the basis of Bayesian inference, a sequential Student’s <em>t</em>-based unscented Kalman filter (SSTUKF), together with its square-root form (SR-SSTUKF), is proposed by using the unscented transform to calculate Student’s <em>t</em> weighted integrals. Considering the nonstationary measurement noise and/or accumulated computation error, adaptive factors are introduced by the <em>t</em>-test to suppress uncertainties. Additionally, the complexity computation and convergence analysis of the SR-SSTUKF are presented. The validity and robustness of the proposed sequential fusion method are illustrated by an example of agile target tracking. Simulation results indicate that the SR-SSTUKF with adaptive factors can further enhance accuracy and yield reliable estimations.</div></div>\",\"PeriodicalId\":14660,\"journal\":{\"name\":\"ISA transactions\",\"volume\":\"156 \",\"pages\":\"Pages 344-364\"},\"PeriodicalIF\":6.3000,\"publicationDate\":\"2025-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ISA transactions\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0019057824005160\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"AUTOMATION & CONTROL SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ISA transactions","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0019057824005160","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0
摘要
本文主要研究具有重尾噪声和缺失测量的多速率多传感器非线性动态系统的序列融合估计。在贝叶斯推理的基础上,提出了一种基于 Student's t 的序列无符号卡尔曼滤波器(SSTUKF)及其平方根形式(SR-SSTUKF),利用无符号变换计算 Student's t 加权积分。考虑到非平稳测量噪声和/或累积计算误差,t 检验引入了自适应因子来抑制不确定性。此外,还介绍了 SR-SSTUKF 的复杂性计算和收敛性分析。以敏捷目标跟踪为例,说明了所提出的顺序融合方法的有效性和鲁棒性。仿真结果表明,带有自适应因子的 SR-SSTUKF 可以进一步提高精度,并产生可靠的估计结果。
Sequential fusion for multi-rate multi-sensor nonlinear dynamic systems with heavy-tailed noise and missing measurements
This paper focuses on sequential fusion estimation for multi-rate multi-sensor nonlinear dynamic systems with heavy-tailed noise and missing measurements. On the basis of Bayesian inference, a sequential Student’s t-based unscented Kalman filter (SSTUKF), together with its square-root form (SR-SSTUKF), is proposed by using the unscented transform to calculate Student’s t weighted integrals. Considering the nonstationary measurement noise and/or accumulated computation error, adaptive factors are introduced by the t-test to suppress uncertainties. Additionally, the complexity computation and convergence analysis of the SR-SSTUKF are presented. The validity and robustness of the proposed sequential fusion method are illustrated by an example of agile target tracking. Simulation results indicate that the SR-SSTUKF with adaptive factors can further enhance accuracy and yield reliable estimations.
期刊介绍:
ISA Transactions serves as a platform for showcasing advancements in measurement and automation, catering to both industrial practitioners and applied researchers. It covers a wide array of topics within measurement, including sensors, signal processing, data analysis, and fault detection, supported by techniques such as artificial intelligence and communication systems. Automation topics encompass control strategies, modelling, system reliability, and maintenance, alongside optimization and human-machine interaction. The journal targets research and development professionals in control systems, process instrumentation, and automation from academia and industry.