Adaptive semi greedy search for multidimensional track assignment
Robust multitarget, multisensor fusion algorithms rely on optimal or near optimal assignment algorithms at their core. Greedy and semi greedy search algorithms represent a fast and effective solution to multi dimensional assignment problems, at the cost of often producing sub optimal results in challenging cases. The Adaptive Semi Greedy Search (ASGS) presented here is an improvement on older semi greedy search methods, and is more likely to find the optimal solution without sacrificing computational speed. ASGS outperforms Semi Greedy Track Selection (SGTS) in a wide range of statistical tests, where it was consistently more likely to find the optimal solution to 6 dimensional assignment problems, as if the RMS measurement error had been reduced by 25% The two algorithms are also compared in several challenging multitarget, multisensor tracking scenarios, including maneuvering formations and crossing targets, where a Multiple Hypothesis Tracker (MHT) using the ASGS significantly reduces the RMS position error of the tracks (p 0.001 for each scenario) The ASGS effectively increases the range of scenarios in which semi greedy assignment algorithms can be reliably employed, especially valuable for real time applications with large number of targets and sensors.
2016 19th International Conference on Information Fusion (FUSION)