Laplace estimation for scalar linear systems

Nhattrieu C. Duong, Jason L. Speyer, Moshe Idan

Research output: Contribution to journalArticlepeer-review

Abstract

Uncertainties in many physical systems have impulsive properties poorly modeled by Gaussian distributions. Refocusing previous work, an estimator is derived for a scalar discrete-time linear system with additive Laplace measurement and process noises. The a priori and a posteriori conditional probability density functions (pdf) of the state given a measurement sequence are propagated recursively and in closed form, and the a posteriori conditional mean and variance are derived analytically from the conditional pdf. A simulation for an estimator is presented, demonstrating marked resilience to large, un-modeled spikes in the measurements.

Original languageEnglish
Article number110301
JournalAutomatica
Volume144
DOIs
StatePublished - Oct 2022

Keywords

  • Estimation and filtering
  • Linear systems

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'Laplace estimation for scalar linear systems'. Together they form a unique fingerprint.

Cite this