Parallel efficiency can be greater than unity
Parallel Computing
Micro Time Cost Analysis of Parallel Computations
IEEE Transactions on Computers
A parallel shortest augmenting path algorithm for the assignment problem
Journal of the ACM (JACM)
SIAM Journal on Computing
A Hierarchical Task Queue Organization for Shared-Memory Multiprocessor Systems
IEEE Transactions on Parallel and Distributed Systems
Parallel Implementation of Multiple Model Tracking Algorithms
IEEE Transactions on Parallel and Distributed Systems
Multitarget Tracking Algorithm Parallelization for Distributed-Memory Computing Systems
HPDC '96 Proceedings of the 5th IEEE International Symposium on High Performance Distributed Computing
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The focus of this paper is to present the results of our investigation and evaluation of various shared-memory parallelizations of the data association problem in multitarget tracking. The multitarget tracking algorithm developed was for a sparse air traffic surveillance problem, and is based on an Interacting Multiple Model (IMM) state estimator embedded into the (2D) assignment framework. The IMM estimator imposes a computational burden in terms of both space and time complexity, since more than one filter model is used to calculate state estimates, covariances, and likelihood functions. In fact, contrary to conventional wisdom, for sparse multitarget tracking problems, we show that the assignment (or data association) problem is not the major computational bottleneck. Instead, the interface to the assignment problem, namely, computing the rather numerous gating tests and IMM state estimates, covariance calculations, and likelihood function evaluations (used as cost coefficients in the assignment problem), is the major source of the workload. Using a measurement database based on two FAA air traffic control radars, we show that a "coarse-grained" (dynamic) parallelization across the numerous tracks found in a multitarget tracking problem is robust, scalable, and demonstrates superior computational performance to previously proposed "fine-grained" (static) parallelizations within the IMM.