A computer program estimates great-circle-arc (GARC) flightpaths of multiple aircraft, given time-tagged point locations from radar sightings of the aircraft. The problem of generating such estimates is called a "clustering" problem, and is solved in this instance by use of a neural-network clustering algorithm. Points are considered in pairs; the likelihood that any two points are on the flightpath of the same aircraft is quantified by an "association value" based on the flight dynamics of the aircraft. The program implements a Boltzmann machine, the sparse architecture of which provides for only partial satisfaction of the constraints of a cost function; this, together with a special graphical interface, serves as an aid in determining GARCs. The neural-network algorithm operates on all points simultaneously and performs a global optimization through simulated annealing; thus, it is in many instances superior to both traditional clustering algorithms that operate on points sequentially, and to other neural-network algorithms that perform local optimization. The neural network can also readily be implemented in hardware.
This work was done by John Spagnuolo, Jr., of Caltech for NASA's Jet Propulsion Laboratory. NPO-20288