Laplace Estimator for Linear Scalar Systems

Nhattrieu Duong, Jason L. Speyer, Jun Yoneyama, Moshe Idan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Uncertainties in many physical systems have impulsive properties poorly modeled by Gaussian distributions. Building on work to develop Cauchy estimators, a Laplace estimator is explored as another heavier-tailed alternative. For a scalar discrete linear system with additive Laplace-distributed process and measurement noises, the unnormalized conditional pdf is recursively and analytically propagated and updated, and its structure is presented. The conditional mean and variance of the Laplace estimator after one update are examined and compared to those of the Cauchy estimator and Kalman filter. Additionally, a 50-step numerical example is presented to demonstrate its superior performance to the Kalman filter in the presence of outliers.

Original languageEnglish
Title of host publication2018 IEEE Conference on Decision and Control, CDC 2018
Pages2283-2290
Number of pages8
ISBN (Electronic)9781538613955
DOIs
StatePublished - 2 Jul 2018
Event57th IEEE Conference on Decision and Control, CDC 2018 - Miami, United States
Duration: 17 Dec 201819 Dec 2018

Publication series

NameProceedings of the IEEE Conference on Decision and Control
Volume2018-December

Conference

Conference57th IEEE Conference on Decision and Control, CDC 2018
Country/TerritoryUnited States
CityMiami
Period17/12/1819/12/18

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Modelling and Simulation
  • Control and Optimization

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