Laplace estimation for scalar linear systems

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

Research output: Contribution to journalArticlepeer-review


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
StatePublished - Oct 2022


  • Estimation and filtering
  • Linear systems

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Electrical and Electronic Engineering


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