Data Association Algorithms for Multiple Target Tracking

摘要

Multi-target tracking (MMT) has many applications, and has therefore been thesubject of considerable investigation. One key aspects of this problem, especially in a dense target environment, is the scan-to-scan correlation or tracking data association problem of assigning measurements to tracks. Various different approaches have been proposed to solve this problem. In this report, three algorithms for MTT data association are presented for comparison. First the nearest-neighbor standard filter (NNSF) algorithms is presented. Then two of the more promissing extensions are presented: the multiple hypothesis test method (MHT), and joint probabilistic data association (JPDA) method. Whenever possible the same notation is used in presenting all three methods for ease of comparison. This report is intended to serve as a prelude to a comparative investigation of these three competing data association methods.