Integrated track splitting-component merging history for efficient target tracking

Abstract

The Integrated Track Splitting (ITS) filter is a method for automatic target tracking in clutter. The ITS filter models each track as a set of components, where each component is defined with a unique measurement history which consists of zero or one measurement received each scan. Regarding to clutter density and tracking parameters, a component merging is used for the reason of computational complexity reducing. For each component the state estimate and the a-posteriori probability of component existence are computed recursively. The probability of the new component existence is the probability that the parent component exists and that the measurement used to create the new component is the target measurement. The probability of target existence, mean and covariance of the state estimate for the track are then calculated and used for track maintenance and track output. Using an extensive Monte Carlo simulation, the various parameters (probability of track elimination and confirmation, probability of detection) are changed to obtain the optimal depth of component merging history (CMH), numbers of confirmed false track or lost tracks that each produces. The article is having a practical contribution while approve which depth of CMH is needed for the specific tracking situation.