Set Theory Correlation Free Algorithm for HRRR Target Tracking
Erik Blasch, Lang Hong · 1999
One challenge of simultaneous tracking and identification of targets is the fusion of continuous and discrete information. Recently a few fusionists including Mahler [1] and Mori [2] are using a set theory approach for a unified data fusion theory which is a correlation free paradigm [3]. This paper uses the set theory approach as a basis for a method of fusing kinematic-continuous data and identification-discrete feature information. The set of features are high range resolution radar range-bin locations and amplitudes which are collected over a small aperture and a scrambled method is used to order a feature set. Once features are ordered, a recursive belief filter operates in feature space to combine track and identification measurements. The intersection of track and identification methods results in a simultaneous tracking and identification algorithm which accumulates evidence for belief in targets and rules out non-plausible targets Multitarget tracking in the presence of clutter has been investigated through the use of data association algorithms [4] such as the joint-probability data association (JPDAF). Likewise, other multisensor fusion algorithms have focused on tracking targets from multiple look sequences such as the multiresolution wavelet-based approach formulated by Hong [5]. One inherent limitation of current algorithms is that the information used to track targets is