Track initialization from incomplete measurements

Abstract

Target tracking from incomplete measurements of distinct sensors in a sensor network is a task of data fusion, present in a lot of applications. Difficulties in tracking using extended Kalman filters lead to unstable behavior, mainly caused by difficult initialization. Instead of using numerical batch-estimators, we offer an analytical approach to initialize the filter from a minimum number of observations. Additionally, we provide the possibility to estimate only sub-sets of parameters, and to reliably model resulting added uncertainties by the covariance matrix. The approach will be studied in two practical examples: 3D track initialization using bearings-only measurements and using slant-range and azimuth only. Numerical results will include performance and consistency analysis via Monte-Carlo simulations and comparison to the Cramer-Rao lower bound.