Abstract
This paper presents a method for tracking dismounts/humans in a potentially dense clutter background. The proposed
approach uses Multiple Hypothesis Tracking (MHT) for data association and Interacting Multiple Model (IMM)
filtering. The problem is made difficult by the presence of random and persistent clutter, such as produced by moving
tree branches. There may also be moving targets (such as vehicles and animals) that are not of interest to the user of the
tracking system, but that must be tracked in order to separate these targets from the targets of interest. Thus, a joint
tracking and identification method has been developed to utilize the features that are associated with dismount targets.
This method uses a Dempster-Shafer (D-S) approach to combine feature data to determine the target type (dismount
versus other). Feature matching is also included in the computation of the track score used for MHT data association.
The paper begins by giving an overview of the features that have been proposed in the literature for distinguishing
humans from other types of targets. These features include radar cross section, target dynamics, and spectral and gait
characteristics. For example, the number of secondary peaks around the main peak corresponding to the mean Doppler
shift is one feature that is sent to the tracker. A large number of secondary peaks will be an indication that the
observation is from an animal, rather than a vehicle. Also, if spectral analysis of the variation in Doppler shift due to
torso motion yields a distinct periodic pattern with a peak at about 2 Hz, this can be used to identify the target as a
human and, along with the target speed, may even be used as a target signature. The manner in which these features are
estimated during signal processing and how this data is included in the track score is described.
A test program conducted to produce data for analysis and development is described. Typical results derived from real
data, collected during this test program, are presented to show how feature data is used to enhance the tracking solution.
These results show that the proposed methods are effective in separating the tracks on dismounts from those formed on
clutter and other objects.
