Multiframe assignment tracker for MSTWG data
Ratnasingham Tharmarasa, S. Sutharsan, Thia Kirubarajan, Thomas Lang · International Conference on Information Fusion · 2009
In this paper, a multiframe assignment tracker is applied to the simulated data sets provided by the Multistatic Tracking Working Group (MSTWG). The multiframe assignment tracker solves the data association problem as a constrained optimization for fusing multiple sets of data to the tracks with an Interacting Multiple Model (IMM) estimator. The challenges with these data sets are high false alarm rate, low probability of detection and multiple synchronous/asynchronous sensors. Multiframe data association is used to perform data association, which is the crucial part of the tracking. Centralized tracking is used to optimally fuse the information from multiple sensors. A track's status is updated using an m out of n logic rather than the track quality based logic that requires more accurate probability of detection values, which are not available and vary with time and geometry in the MSTWG data sets. The results obtained with the multiframe assignment tracker for all the data sets are given in the form of MSTWG performance metrics.