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Developing the information-analysis tools for an effective multi-agency information-sharing effort.

Real-time optimal filtering for stochastic systems with multiresolutional measurements. Comparison of two-sensor tracking methods based on state vector fusion and measurement fusion. The interacting multiple model (IMM) estimator has been shown to be very effective when applied to air traffic surveillance problems. 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 systems 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.

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In recent years, there has been considerable interest within the tracking community in an approach to data association based on the m-best two-dimensional (2-D) assignment algorithm. Much of the interest has been spurred by its ability to provide various efficient data association solutions, including joint probabilistic data association (JPDA) and multiple hypothesis tracking (MHT). The focus of this work is to describe several recent improvements to the m-best 2-D assignment algorithm. One improvement is to utilize a nonintrusive 2-D assignment algorithm switching mechanism, based on a problem sparsity threshold. Dynamic switching between two different 2-D assignment algorithms, highly suited for sparse and dense problems, respectively, enables more efficient solutions to the numerous 2-D assignment problems generated in the m-best 2-D assignment framework. Another improvement is to utilize a multilevel parallelization enabling many independent and highly parallelizable tasks to be executed concurrently, including 1) solving the multiple 2-D assignment problems via a parallelization of the m-best partitioning task, and 2) calculating the numerous gating tests, state estimates, covariance calculations, and likelihood function evaluations (used as cost coefficients in the 2-D assignment problem) via a parallelization of the data association interface task. Using both simulated data and an air traffic surveillance (ATS) problem based on data from two Federal Aviation Administration (FAA) air traffic control radars, we demonstrate that efficient solutions to the data association problem are obtainable using our improvements in the m-best 2-D assignment algorithm.