In this paper we describe a novel data association, termed m-best SD, that determines in O(mSkn3) time the m-best solutions to an SD assignment problem. THis algorithm is applied to the following problem. Given line of sight measurements from S sensors, sets of complete position measurements are extracted, namely, the 1st, 2nd,..., m-th best sets of composite measurements are determined solving a static SD assignment problem. Utilizing the joint likelihood functions used to determine the m-best SD assignment solutions, the composite measurements are then quantified with a probability of being correct using a JPDA-like technique. Lists of composite measurements from successive scans, along with their corresponding probabilities, are then used in turn with a state estimator in a dynamic 2D assignment algorithm to estimate the states of the moving targets over time. The dynamic assignment cost coefficients are based on a likelihood function that incorporates the 'true' composite measurement probabilities obtained from the (static) m-best SD assignment solutions. We demonstrate this algorithm on a multitarget passive sensor track formation and maintenance problem, consisting of multiple time samples of line of sight measurements originating from multiple synchronized high frequency direction finding sensors. Another significance of this work is that the m-best SD assignment algorithm provides for an efficient implementation of a multiple hypothesis tracking algorithm by obviating the need for a brute force enumeration of an exponential number of joint hypotheses.
In this paper, we are concerned with the problem of assigning track tasks, with uncertain processing costs and negligible communication costs, across a set of homogeneous processors within a distributed computing system to minimize workload imbalances. Since the task processing cost is uncertain at the time of task assignment, we propose several fast heuristic solutions that are extensible, incur very little overhead, and typically react well to changes in the state of the workload. The primary differences between the task assignment algorithms proposed are: (i) the definition of a task assignment cost as a function of past, present, and predicted workload distribution, (ii) whether or not information sharing concerning the state of the workload occurs among processors, and (iii) if workload state information is shared, the reactiveness of the algorithm to such information (i.e., high-pass, moderate, low-pass information filtering). We show, in the context of a multisensor-multitarget tracking problem, that using the heuristic task assignment algorithms proposed can yield excellent results and offer great promise in practice.
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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System Diagnosis and Prognosis: Security and Condition Monitoring Issues III
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