A branching algorithm for discriminating and tracking multiple objects

A recursive branching algorithm for multiple-object discrimination and tracking consists of a bank of parallel filters of the Kalman form, each of which estimates a trajectory associated with a certain selected measurement sequence. The measurement sequences processed by the algorithm are restricted to a tractable number by combining similar trajectory estimates, by excluding unlikely measurement/state associations, and by deleting unlikely trajectory estimates. The measurement sequence selection is accomplished by threshold tests based on the innovations sequence and state estimates of each filter. Numerical experiments performed using the algorithm illustrate how the accuracy of the a priori state estimates and trajectory model influences the selectivity of the algorithm.