Richard R. Gassner

University of Connecticut, Storrs, CT, United States

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Publications (5)0 Total impact

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    ABSTRACT: We have developed a completely automated approach to hardware design based on integrating three core technologies into one comprehensive system, namely genetic programming (GP), the VHSIC Hardware Description Language (VHDL) and field programmable gate arrays (FPGAs). Our system uses an automated GP engine, as opposed to a human designer, to evolve a hardware design composed of one or more FPGAs that will maximally achieve an application's software requirements. Several variants of our system exist; other variants are currently under development. The focus of this paper is to describe our original system design and its most recent revision to date
    Systems, Man, and Cybernetics, 1998. 1998 IEEE International Conference on; 11/1998
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    ABSTRACT: In this paper we describe a novel data association algorithm and parallelization, termed m-best SD, that determines in O(mSkn<sup>3</sup>) time (m assignments, S lists of size n, k relaxations) the m-best solutions to an SD assignment problem. The significance of this work is that the m-best SD assignment algorithm (in a sliding window mode) provides for an efficient implementation of an (S-1)-scan Multiple Hypothesis Tracking (MHT) algorithm by obviating the need for a brute force enumeration of an exponential number of joint hypotheses. Initially, given a static SD assignment problem, sets of complete position measurements are extracted, namely, the 1-st, 2-nd, ..., m-th best (in terms of likelihood) sets of composite measurements are determined based on the line of sight (LOS) (i.e., incomplete position) measurements. Using 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, 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 2D 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 m-best SD on a simulated passive sensor track formation and maintenance problem, consisting of multiple time samples of LOS measurements originating from multiple (S=7) synchronized high frequency direction finding sensors
    Aerospace Conference, 1998 IEEE; 04/1998
  • Robert L. Popp, Krishna R. Pattipati, Richard R. Gassner
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    ABSTRACT: 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.
    Proc SPIE 05/1996;
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    ABSTRACT: We present a robust scalable parallelization of a multitarget tracking algorithm developed for air traffic surveillance. We couple the state estimation and data association problems by embedding an interacting multiple model (IMM) state estimator into an optimization-based assignment framework. A SPMD distributed-memory parallelization is described wherein the interface to the optimization problem, namely computing the rather numerous gating and IMM state estimates, covariance calculations, and likelihood function evaluations (used as cost coefficients in the assignment problem), is parallelized. We describe several heuristic algorithms developed for the inherent task allocation problem wherein the problem is one of assigning track tasks, having uncertain processing costs and negligible communication costs, across a set of homogeneous processors to minimize workload imbalances. Using a measurement database based on two FAA air traffic central radars, courtesy of Rome Laboratory, we show that near linear speedups are obtainable on a 32-node Intel Paragon supercomputer using simple task allocation algorithms.
    High Performance Distributed Computing, 1996., Proceedings of 5th IEEE International Symposium on; 01/1996
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    ABSTRACT: This paper deals with the design and implementation of MATSurv 1--an experimental Multisensor Air Traffic Surveillance system. The proposed system consists of a Kalman filter based state estimator used in conjunction with a 2D sliding window assignment algorithm. Real data from two FAA radars is used to evaluate the performance of this algorithm. The results indicate that the proposed algorithm provides a superior classification of the measurements into tracks (i.e., the most likely aircraft trajectories) when compared to the aircraft trajectories obtained using the measurement IDs (squawk or IFF code).
    Proc SPIE 01/1995;