Multi-Sensor Marginalized Particle Filtering for Dynamic Source Estimation

IF 2.4 Q2 AUTOMATION & CONTROL SYSTEMS
Nicola Forti;Giorgio Battistelli;Luigi Chisci
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Abstract

This letter presents a marginalized particle filtering method for localizing, from sparse measurements, a moving source emitting a spatio-temporal field governed by a partial differential equation (PDE). We explicitly consider the full space-time dynamics of the field using a finite-element (FE) approximation for the spatial discretization of the governing PDE system. We propose a marginalized (or Rao-Blackwellized) formulation of the joint field and source estimation problem that leverages the conditionally linear-Gaussian structure of the system with respect to the source position and intensity. This formulation enables the estimation of field variables conditioned on each source position particle using the optimal Kalman filter. We apply this marginalized formulation to both centralized and distributed multi-sensor architectures with remarkable results in terms of monitoring performance and computational efficiency.
用于动态源估计的多传感器边际粒子滤波技术
这封信提出了一种边际粒子滤波方法,用于根据稀疏测量结果,定位一个发射受偏微分方程(PDE)控制的时空场的移动源。我们使用有限元(FE)近似法对支配偏微分方程系统进行空间离散化,明确考虑了场的全时空动态。我们提出了联合场和源估计问题的边际化(或 Rao-Blackwellized )公式,利用了系统中与源位置和强度相关的条件线性高斯结构。通过这种表述方式,可以使用最优卡尔曼滤波器估算以每个源位置粒子为条件的场变量。我们将这种边际化公式应用于集中式和分布式多传感器架构,在监测性能和计算效率方面都取得了显著效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
IEEE Control Systems Letters
IEEE Control Systems Letters Mathematics-Control and Optimization
CiteScore
4.40
自引率
13.30%
发文量
471
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