Quanta tracking algorithm for multiple moving targets

The Quanta Tracking (QT) algorithm was created by this team in 2013. The algorithm was originally called the Maximum Likelihood, Histogram Probabilistic Multi-Hypothesis Tracker (ML-HPMHT). While this gives some insight into the functionality contained in the algorithm it is too much for the average human to say without twisting a tongue. The QT algorithm is showing excellent results at tracking unresolved, dim targets in highly cluttered environments. Traditional detection and tracking approaches use thresholding and signal processing to declare measurements which are then fed into the tracker. The QT algorithm does this all organically in an optimal manner, called “track-before-detect”. The algorithm requires no thresholding of the data such that all of the data is utilized. The advancement in this paper is accounting for stationary targets and not tracking them.