分布状态估计的一致迭代后验线性化滤波器

IF 3.2 2区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Signal Processing Letters Pub Date : 2025-01-06 DOI:10.1109/LSP.2025.3526092

Ángel F. García-Fernández;Giorgio Battistelli

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引用次数: 0

摘要

提出了一种用于分布状态估计的一致迭代后验线性化滤波器。共识IPLF算法基于给定状态下的条件均值和协方差描述的测量模型，并对测量值相对于当前后验近似值进行迭代统计线性回归，以提高估计性能。基于所使用的共识类型，提出了该算法的三种变体：信息共识、测量共识和测量和信息混合共识。仿真结果表明了该算法在分布式状态估计中的优越性。

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Consensus Iterated Posterior Linearization Filter for Distributed State Estimation

This paper presents the consensus iterated posterior linearisation filter (IPLF) for distributed state estimation. The consensus IPLF algorithm is based on a measurement model described by its conditional mean and covariance given the state, and performs iterated statistical linear regressions of the measurements with respect to the current approximation of the posterior to improve estimation performance. Three variants of the algorithm are presented based on the type of consensus that is used: consensus on information, consensus on measurements, and hybrid consensus on measurements and information. Simulation results show the benefits of the proposed algorithm in distributed state estimation.

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来源期刊

IEEE Signal Processing Letters 工程技术-工程：电子与电气

CiteScore

7.40

自引率

12.80%

发文量

339

审稿时长

2.8 months

期刊介绍： The IEEE Signal Processing Letters is a monthly, archival publication designed to provide rapid dissemination of original, cutting-edge ideas and timely, significant contributions in signal, image, speech, language and audio processing. Papers published in the Letters can be presented within one year of their appearance in signal processing conferences such as ICASSP, GlobalSIP and ICIP, and also in several workshop organized by the Signal Processing Society.