Relative Track Metrics to Determine Model Mismatch

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.