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Computational Requirements of a Non-combinatorial Detection of Multiple Targets in High GMTI Clutter

The major computational challenge of multi-target tracking in clutter is solving the report-to-track association problem. Multiple hypothesis tracking (MHT) [4] is an approximate solution that tries to limit the combinatorial complexity of assignment data to models in multiple frames. MHT processing and memory requirements grow exponentially with the increased number of frames used to resolve the associations [5]. In addition, a real-time realization of an MHT tracker is difficult due to the complexity of data movement required to manage track hypotheses. This data movement leads to inefficient utilization of embedded processors.