Skip to main navigation Skip to search Skip to main content

A comparative study of nonlinear filters for target tracking in mixed coordinates

  • University of New Orleans

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

19 Scopus citations

Abstract

The measurement model nonlinearity is a major challenge in target tracking. This paper presents a comparative performance study of seven nonlinear filters in handling the measurement model nonlinearity. They are: the extended Kalman filter, the unscented filter, the second order divided-differences filter, the Gauss-Hermite quadrature filter, the two-step Kalman filter, the Gaussian particle filter, and the linear minimum mean-square error tracking filter with polar measurements. Comprehensive performance evaluation and comparison of all of the above mainstream nonlinear filters over the same tracking scenarios are conducted via Monte Carlo simulation. The results can facilitate the choice and design of nonlinear tracking filters in mixed coordinates.

Original languageEnglish
Title of host publication2010 42nd Southeastern Symposium on System Theory, SSST 2010
Pages202-207
Number of pages6
DOIs
StatePublished - 2010
Externally publishedYes
Event2010 42nd Southeastern Symposium on System Theory, SSST 2010 - Tyler, TX, United States
Duration: 7 Mar 20109 Mar 2010

Publication series

NameProceedings of the Annual Southeastern Symposium on System Theory

Conference

Conference2010 42nd Southeastern Symposium on System Theory, SSST 2010
Country/TerritoryUnited States
CityTyler, TX
Period7/03/109/03/10

Keywords

  • Nonlinear filters
  • Target tracking

Fingerprint

Dive into the research topics of 'A comparative study of nonlinear filters for target tracking in mixed coordinates'. Together they form a unique fingerprint.

Cite this