Adaptive filtering for single target tracking

Maria Scalzo, Gregory Horvath, Eric Jones, Adnan Bubalo, Mark Alford, Ruixin Niu, Pramod K. Varshney

Research output: Chapter in Book/Entry/PoemConference contribution

6 Scopus citations


Many algorithms may be applied to solve the target tracking problem, including the Kalman Filter and different types of nonlinear filters, such as the Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF) and Particle Filter (PF). This paper describes an intelligent algorithm that was developed to elegantly select the appropriate filtering technique depending on the problem and the scenario, based upon a sliding window of the Normalized Innovation Squared (NIS). This technique shows promise for the single target, single radar tracking problem domain. Future work is planned to expand the use of this technique to multiple targets and multiple sensors.

Original languageEnglish (US)
Title of host publicationSignal Processing, Sensor Fusion, and Target Recognition XVIII
StatePublished - 2009
EventSignal Processing, Sensor Fusion, and Target Recognition XVIII - Orlando, FL, United States
Duration: Apr 13 2009Apr 15 2009

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X


OtherSignal Processing, Sensor Fusion, and Target Recognition XVIII
Country/TerritoryUnited States
CityOrlando, FL


  • Consistency checks
  • Extended kalman filter
  • Filter suite
  • Particle filter
  • Tracking
  • Unscented kalman filter

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


Dive into the research topics of 'Adaptive filtering for single target tracking'. Together they form a unique fingerprint.

Cite this