Least-squares linear filtering using observations coming from multiple sensors with one- or two-step random delay

DOI10.1016/j.sigpro.2009.04.007zbMath1178.94142OpenAlexW2042974439WikidataQ59552396 ScholiaQ59552396MaRDI QIDQ1032440

José Domingo Jiménez-López, Josefa Linares-Pérez, Raquel Caballero-Águila, Aurora Hermoso-Carazo

Publication date: 26 October 2009

Published in: Signal Processing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.sigpro.2009.04.007

zbMATH Keywords

innovation processleast-squares estimationcovariance informationdiscrete-time stochastic systemsdelayed measurementsmultiple sensor systems

Mathematics Subject Classification ID

Estimation and detection in stochastic control theory (93E10) Detection theory in information and communication theory (94A13)

Related Items (10)

Robust variance-constrained filtering for a class of nonlinear stochastic systems with missing measurements ⋮ A novel model for linear dynamic system with random delays ⋮ Recursive estimation for dynamical systems with different delay rates sensor network and autocorrelated process noises ⋮ Recent advances on recursive filtering and sliding mode design for networked nonlinear stochastic systems: a survey ⋮ Information fusion algorithms for state estimation in multi-sensor systems with correlated missing measurements ⋮ Descriptor recursive estimation for multiple sensors with different delay rates ⋮ A new estimation algorithm from measurements with multiple-step random delays and packet dropouts ⋮ Distributed and centralized fusion estimation from multiple sensors with Markovian delays ⋮ Decentralized robust set-valued state estimation in networked multiple sensor systems ⋮ Covariance-based least-squares filtering algorithm under Markovian measurement delays

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