Parallelization of a multiple model multitarget tracking algorithm with superlinear speedups

The interacting multiple model (IMM) estimator has been shown to be very effective when applied to air traffic surveillance problem. However, because of the additional filter modules necessary to cover the possible target maneuvers, the IMM estimator also imposes an increasing computational burden. Hence, in an effort to design a real-time multiple model multitarget tracking algorithm that is independent of the number of modules used in the state estimator, we propose a "coarse-grained" (dynamic) parallelization that is superior, in terms of computational performance, to a "fine-grained" (static) parallelization of the state estimator, while not sacrificing tracking accuracy. In addition to having the potential of realizing superlinear speedups, the proposed parallelization scales to larger multiprocessor system and is robust, i.e., it adapts to diverse multitarget scenarios maintaining the same level of efficiency given any one of numerous factors influencing the problem size. We develop and demonstrate the dynamic parallelization on a shared-memory MIMD multiprocessor for a civilian air traffic surveillance problem using a measurement database based on two FAA air traffic control radars.