Parallelization of a large-scale IMM-based multitarget tracking algorithm

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Abstract
The Interacting Multiple Model (IMM) estimator has been shown to be superior, in terms of tracking accuracy, to a well-tuned Kalman filter when applied to tracking maneuvering targets. However, because of the increasing number of filter modules necessary to cover the possible target maneuvers, the IMM estimator also imposes an additional computational burden. Hence, in an effort to design a real-time IMM-based multitarget tracking algorithm that is independent of the number of modules used in the IMM estimator, we propose a `coarse- grained' (dynamic) parallel implementation that is superior, in terms of computational performance, to previous `fine-grained' (static) parallelizations of the IMM estimator. In addition to having the potential of realizing superlinear speedups, the proposed implementation scales to larger multiprocessor systems and is robust. We demonstrate the performance results both analytically and using a measurement database from two FAA air traffic control radars.

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