Abstract
Clustering algorithms have been recently used in multitarget multisensor tracking (MMT) problems in order to reduce the size of the data association problem. This paper derives an equivalence relation (ER) clustering algorithm used in a MMT problem and briefly compares it to other clustering schemes such as the nearest neighbor method. The main contribution of this work is the analytical evaluation of ER clustering performance, in the context of multitarget multisensor tracking, as a function of the distance between targets, measurement probability density function, and cluster parameter.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have