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IEEE Regional Conference on Aerospace control systems

作者： Chin-Der Wann S.C.A. Thomopoulos Decision and Control Systems Laboratory Department of Electrical and Computer Engineering Pennsylvania State University University Park PA USA

A benchmark study of two self-organizing artificial neural network models, ART2 and DIGNET, is conducted. The architecture differences and learning procedures between these two models are compared. The performance of ART2 and DIGNET on data clustering and signal detection problems with noise or interference is investigated by comparative simulations. It is shown that DIGNET generally has faster learning and better clustering performance on the statistical pattern recognition problems. DIGNET has a simpler architecture, and the system parameters can be analytically determined from the self-organizing process. The threshold value used in DIGNET can be specifically determined from a given lower bound on the desirable signal-to-noise ratio (SNR). The networks discussed in this paper are applied and benchmarked against clustering and signal detection problems.

关键词： Neural networks Intelligent networks Signal detection Artificial neural networks Pattern recognition Supervised learning Clustering algorithms Signal processing Resonance Partitioning algorithms

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MULTIRESOLUTION STOCHASTIC-MODELS, DATA FUSION, AND WAVELET TRANSFORMS

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SIGNAL PROCESSING 1993年第3期34卷 257-282页

作者： CHOU, KC GOLDEN, SA WILLSKY, AS SRI International 333 Ravenswood Ave. Menlo Park CA 94025 USA Sparta Inc. 23041 Avenida de la Carlota Suite 400 Laguna Hills CA 92653 USA Laboratory for Information and Decision Systems and Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA 02139 USA

In this paper we describe and analyze a class of multiscale stochastic processes which are modeled using dynamic representations evolving in scale based on the wavelet transform. The statistical structure of these models is Markovian in scale, and in addition the eigenstructure of these models is given by the wavelet transform. The implication of this is that by using the wavelet transform we can convert the apparently complicated problem of fusing noisy measurements of our process at several different resolutions into a set of decoupled, standard recursive estimation problems in which scale plays the role of the time-like variable. In addition we show how the wavelet transform, which is defined for signals that extend from - infinity to + infinity, can be adapted to yield a modified transform matched to the eigenstructure of our multiscale stochastic models over finite intervals. Finally, we illustrate the promise of this methodology by applying it to estimation problems, involving single and multi-scale data, for a first-order Gauss-Markov process. As we show, while this process is not precisely in the class we define, it can be well-approximated by our models, leading to new, highly parallel and scale-recursive estimation algorithms for multi-scale data fusion. In addition our framework extends immediately to 2D signals where the computational benefits are even more significant.

关键词： MULTISCALE OPTIMAL ESTIMATION SENSOR FUSION STOCHASTIC MODELING WAVELET TRANSFORM

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Multi-time scale zero-sum differential games with perfect state measurements

Multi-time scale zero-sum differential games with perfect st...

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IEEE Conference on decision and control

作者： Zigang Pan T. Basar Decision and Control Laboratory Coordinated Science Laboratory and the Department of Electrical and Computer Engineering University of Illinois Urbana IL USA

Obtains necessary and sufficient conditions for the existence of "approximate" saddle-point solutions in linear-quadratic zero-sum differential games when the state dynamics are defined on multiple (three) time scales. These different time scales are characterized in terms of two small positive parameters /spl epsiv//sub 1/ and /spl epsiv//sub 2/, and the terminology "approximate saddle-point solution" is used to refer to saddle-point policies that do not depend on /spl epsiv//sub 1/ and /spl epsiv//sub 2/, while providing cost levels within O(/spl epsiv//sub 1/) of the full-order game. It is shown in the paper that, under perfect state measurements, the original game can be decomposed into three subgames - slow, fast and fastest, the composite saddle-point solution of which make up the approximate saddle-point solution of the original game. Specifically, for the minimizing player, it is necessary to use a composite policy that uses the solutions of all three subgames, whereas for the maximizing player, it is sufficient to use a slow policy. In the finite-horizon case this slow policy could be a feedback policy, whereas in the infinite-horizon case it has to be chosen as an open-loop policy that is generated from the solution and dynamics of the slow subgame. These results have direct applications in the H/sup /spl infin//-optimal control of three-time scale singularly perturbed linear systems under perfect state measurements.< >

关键词： Riccati equations Costs Open loop systems control systems Differential equations Differential algebraic equations Coordinate measuring machines Electric variables measurement Time measurement Sufficient conditions

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Target Detection and Localization from Bearing-Only and Bearing/range Measurements

Target Detection and Localization from Bearing-Only and Bear...

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IEEE Regional Conference on Aerospace control systems

作者： L.G. Weiss S.C.A. Thomopoulos Applied Research Laboratory Pennsylvania State University PA USA Decision and Control Systems Laboratory Department of Electrical and Computer Engineering Pennsylvania State University PA USA

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Optical DPSK with generalized phase noise model and narrowband reception

Optical DPSK with generalized phase noise model and narrowba...

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IEEE International Conference on Communications (ICC)

作者： M. Azizoglu P.A. Humblet Department of Electrical Engineering and Computer Science George Washington University USA Laboratory for Information and Decision Systems Massachusetts Institute of Technology USA

The performance of a narrowband differential phase shift keying (DPSK) receiver is analyzed in the case where the phase of the received signal is impaired by laser phase noise. This receiver is commonly used for the reception of DPSK modulated signals corrupted by additive white Gaussian noise. The effects of both additive and phase noises are fully taken into account. A general phase noise model is employed, which includes the Brownian motion model as a special case. This treatment enables a performance evaluation under feedback control of frequency noise. Numerical results indicate a superior performance, due to both the narrowband nature of the receiver and the phase noise stabilization mechanism.< >

关键词： Optical noise Differential quadrature phase shift keying Phase noise Narrowband Optical receivers Optical feedback Optical modulation Differential phase shift keying Signal analysis Performance analysis

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High Resolution Target Range Estimation in the Presence of Multipath Using JDEF

High Resolution Target Range Estimation in the Presence of M...

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IEEE Regional Conference on Aerospace control systems

作者： M.D. Reger S.C.A. Thomopoulos Applied Research Laboratory Pennsylvania State University University Park PA USA Decision and Control Systems Laboratory Department of Electrical and Computer Engineering Pennsylvania State University University Park PA USA

The Joint Detection/Estimation Filter (JDEF) [1,2] can be utilized to improve the performance of a single Extended Kalmart Filter (EKF) when the problem of estimation involves a large initial variance. In this case the JDEF is used to estimate the presence and location of a point target when the received signal is corrupted by multipath. In the formulation of the problem it was assumed that both the target and sensor were stationary and that there was an equal probability of the point target being present or absent along a center look direction. This implementation of the JDEF proved to be quite successful and can be easily expanded to estimate a larger number of unknown parameters.

关键词： Filters State estimation Laboratories Delay estimation Underwater tracking Mean square error methods control systems Sensor phenomena and characterization Sensor systems Gaussian noise

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High Resolution Multi-Radar Data Fusion Using JDEF

High Resolution Multi-Radar Data Fusion Using JDEF

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IEEE Regional Conference on Aerospace control systems

作者： T.W. Hilands S.C.A. Thomopoulos Applied Research Laboratory Pennsylvania State University University Park PA USA Decision Control Systems Laboratory Department of Electrical and Computer Engineering Pennsylvania State University University Park PA USA

This paper addresses the problem of multi sensor detection and high resolution signal parameter estimation using joint maximum a posteriori detection and high order nonlinear filtering techniques. The model-based fusion approach offers the potential for increased target resolution in range/azimuth space. The technique employs joint detection/estimation filters (JDEF) for target detection and parameter localization. The JDEF approach segments the aggregate nonlinear model over the entire target resolution space into subcells. This partitioning leads to extremely accurtate detection and parameter estimation. The proposed JDEF approach has a built-in capability for automatic data alignment from multiple sensors, and can be used for centralized, noncentralized, and distributed data fusion.

关键词： Sensor arrays Sensor fusion Radar tracking Phased arrays Delay estimation Phase estimation Delay effects Sea measurements Laboratories Signal resolution

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High Resolution Model Order Selection and Parameter Estimation Using JDEF (Joint Detection/Estimation Filter)

High Resolution Model Order Selection and Parameter Estimati...

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IEEE Regional Conference on Aerospace control systems

作者： S.C.A. Thomopoulos K. Ramaswamy G. Rajagopal T.W. Hilands Decision & Control Systems Laboratory Department of Electrical & Computer Engineering Pennsylvania State University University Park PA USA Applied Research Laboratory Pennsylvania State University University Park PA USA

The problem of model order selection in the presence of parametric uncertainty is considered. A high resolution joint detection/estimation filter is used to detect the correct order of the model and estimate the signa... 详细信息

关键词： Parameter estimation Radar detection Acoustic signal detection Filters Additive noise Predictive models Laboratories Mathematical model State estimation Higher order statistics

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High Order Filters for Estimation in Non-Gaussian Noise

High Order Filters for Estimation in Non-Gaussian Noise

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American control Conference (ACC)

作者： Stelios C. A. Thomopoulos Thomas W. Hilands Decision and Control Systems Laboratory Department of Electrical and Computer Engineering Pennsylvania State University University Park PA USA Applied Research Laboratory Pennsylvania State University University Park PA USA

In this paper high order vector filter equations are developed for estimation in non-Gaussian noise. The difference between the filters developed here and the standard Kalman filter is that the filter equation contains nonlinear functions of the innovations process. These filters are general in that the initial state covariance, the measurement noise covariance, and the process noise covariance can all have non-Gaussian distributions. Two filter structures are developed. The first filter is designed for systems with asymmetric probability densities. The second is designed for systems with symmetric probability densities. Experimental evaluation of these filters for estimation in non-Gaussian noise, formed from Gaussian sum distributions, shows that these filters perform much better than the standard Kalman filter, and close to the optimal Bayesian estimator.

关键词： Filters Bayesian methods Noise measurement State estimation Laboratories Nonlinear equations Technological innovation Vectors control systems Standards development

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Nonlinear Adaptive Joint Detection/Estimation for Multi-Radar Data Fusion

Nonlinear Adaptive Joint Detection/Estimation for Multi-Rada...

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American control Conference (ACC)

作者： Thomas W. Hilands Stelios C. A. Thomopoulos Applied Research Laboratory Pennsylvania State University University Park PA USA Decision and Control Systems Laboratory Department of Electrical and Computer Engineering Pennsylvania State University University Park PA USA

This paper addresses the problem of multisensor detection and high resolution signal parameter estimation using joint maximum a posteriori detection and high order nonlinear filtering techniques. The model-based fusion approach offers the potential for increased target resolution in range/azimuth space. The technique employs joint detection/estimation (JDE) filters for target detection and target parameter localization. The JDE approach segments the aggregate nonlinear model over the entire target resolution space into resolution subcells. This partitioning leads to extremely accurtate detection and parameter estimation. The proposed JDE approach has a built-in capability for automatic data alignment from multiple sensors, and can be used for centralized, noncentralized, and distributed data fusion.

关键词： Sensor arrays Parameter estimation Phased arrays State estimation Sensor fusion Radar tracking Clutter Delay effects Sea measurements Signal resolution

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