Quanta tracking algorithm for multiple targets with velocity

The Quanta Tracking (QT) algorithm is a fairly new algorithm that is showing very promising results tracking unresolved, dim targets in highly cluttered environments. Traditional detection and tracking approaches use thresholding and signal processing to declare measurements that 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. In the latest paper on this algorithm, we wrote about accounting for stationary targets and not tracking them. In this paper, we outlined two approaches for not tracking stationary targets. The first approach, implemented previously, removed any tracks that were below a certain velocity threshold. This allowed the algorithm to process the energy from these “stationary” targets, but then removed them from the set of tracks reported. Now we have implemented a second approach that prevents these “stationary” tracks from forming during the processing of the data. This approach has an advantage in that the algorithm will not spend any resources on these undesired stationary targets. In this paper, we show results from this latest approach as well as compare these results against the previous approach for not tracking stationary targets.