NDLI: Relative Track Metrics to Determine Model Mismatch

Content Provider	IEEE Xplore Digital Library
Author	Blasch, E. Rice, A. Chun Yang Kadar, I.
Copyright Year	2008
Description	Author affiliation: Sigtem Technol. Inc., Harleysville, PA (Chun Yang) \|\| Interlink Sys. Sci. Inc., Lake Success, NY (Kadar, I.) \|\| Air Force Res. Lab., Wright Patterson Afb, OH (Blasch, E.; Rice, A.)
Abstract	Tracking performance is a function of data quality, tracker type, and target maneuverability. Many contemporary tracking methods are useful for various operating conditions. To determine nonlinear tracking performance independent of the scenario, we wish to explore metrics that highlight the tracker capability. With the emerging relative track metrics, as opposed to root-mean-square error (RMS) calculations, we explore the Averaged Normalized Estimation Error Squared (ANESS) and Non Credibility Index (NCI) to determine tracker quality independent of the data. This paper demonstrates the usefulness of relative metrics to determine a model mismatch, or more specifically a bias in the model, using the probabilistic data association filter, the unscented Kalman filter, and the particle filter.
Starting Page	257
Ending Page	264
File Size	480104
Page Count	8
File Format	PDF
ISBN	9781424426157
ISSN	79640977
DOI	10.1109/NAECON.2008.4806556
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-07-16
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Target tracking Particle filters Aerospace electronics Monte Carlo methods Lakes Estimation error Root mean square Sensor systems Particle tracking Probability PDAF Relative track metrics NCI PF UKF
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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Multitarget tracking performance analysis using the non-credibility index in the Nonlinear Estimation Framework (NEF) toolbox

Improved particle filters for ballistic target tracking

Track Fusion with Legacy Track Sources

Study of Multi-target Tracking and Data Association Based on Sequential Monte Carlo Algorithm

Posterior Cramér-Rao bound for range-based target tracking in sensor networks

Bayesian model selection for multisensor track-to-track association and track fusion

Particle Filtering for Target Tracking with Mobile Sensors

Online Multiple Target Tracking and Sensor Registration Using Sequential Monte Carlo Methods

Added Markov Chain Monte Carlo particle filtering optimized by Mean Shift and application in target tracking

Relative Track Metrics to Determine Model Mismatch

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