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Proceedings of the IEEE

Issue 3 • Date March 2004

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Displaying Results 1 - 19 of 19
  • [Front cover]

    Publication Year: 2004, Page(s): c1
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  • Proceedings of the IEEE celebrating 92 years of in-depth coverage on emerging technologies

    Publication Year: 2004, Page(s): c2
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  • Table of contents

    Publication Year: 2004, Page(s): 397
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  • Proceedings of the IEEE society information

    Publication Year: 2004, Page(s): 398
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  • Special Issue on Sequential State Estimation

    Publication Year: 2004, Page(s):399 - 400
    Cited by:  Papers (17)
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  • Unscented filtering and nonlinear estimation

    Publication Year: 2004, Page(s):401 - 422
    Cited by:  Papers (1329)  |  Patents (9)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (664 KB) | HTML iconHTML

    The extended Kalman filter (EKF) is probably the most widely used estimation algorithm for nonlinear systems. However, more than 35 years of experience in the estimation community has shown that is difficult to implement, difficult to tune, and only reliable for systems that are almost linear on the time scale of the updates. Many of these difficulties arise from its use of linearization. To overc... View full abstract»

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  • Particle methods for change detection, system identification, and control

    Publication Year: 2004, Page(s):423 - 438
    Cited by:  Papers (87)  |  Patents (3)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (424 KB) | HTML iconHTML

    Particle methods are a set of powerful and versatile simulation-based methods to perform optimal state estimation in nonlinear non-Gaussian state-space models. The ability to compute the optimal filter is central to solving important problems in areas such as change detection, parameter estimation, and control. Much recent work has been done in these areas. The objective of this paper is to provid... View full abstract»

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  • Bayesian sequential state estimation for MIMO wireless communications

    Publication Year: 2004, Page(s):439 - 454
    Cited by:  Papers (33)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (576 KB) | HTML iconHTML

    This paper explores the use of particle filters, rooted in Bayesian estimation, as a device for tracking statistical variations in the channel matrix of a narrowband multiple-input, multiple-output (MIMO) wireless channel. The motivation is to permit the receiver to acquire channel state information through a semiblind strategy and thereby improve the receiver performance of the wireless communica... View full abstract»

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  • Diagnosis by a waiter and a Mars explorer

    Publication Year: 2004, Page(s):455 - 468
    Cited by:  Papers (26)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (736 KB) | HTML iconHTML

    This paper shows how state-of-the-art state estimation techniques can be used to provide efficient solutions to the difficult problem of real-time diagnosis in mobile robots. The power of the adopted estimation techniques resides in our ability to combine particle filters with classical algorithms, such as Kalman filters. We demonstrate these techniques in two scenarios: a mobile waiter robot and ... View full abstract»

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  • Real-time particle filters

    Publication Year: 2004, Page(s):469 - 484
    Cited by:  Papers (82)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1168 KB) | HTML iconHTML

    Particle filters estimate the state of dynamic systems from sensor information. In many real-time applications of particle filters, however, sensor information arrives at a significantly higher rate than the update rate of the filter. The prevalent approach to dealing with such situations is to update the particle filter as often as possible and to discard sensor information that cannot be process... View full abstract»

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  • Real-time speaker tracking using particle filter sensor fusion

    Publication Year: 2004, Page(s):485 - 494
    Cited by:  Papers (40)  |  Patents (10)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (392 KB) | HTML iconHTML

    Sensor fusion for object tracking has become an active research direction during the past few years. But how to do it in a robust and principled way is still an open problem. In this paper, we propose a new fusion framework that combines both the bottom-up and top-down approaches to probabilistically fuse multiple sensing modalities. At the lower level, individual vision and audio trackers are des... View full abstract»

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  • Data fusion for visual tracking with particles

    Publication Year: 2004, Page(s):495 - 513
    Cited by:  Papers (218)  |  Patents (39)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1360 KB) | HTML iconHTML

    The effectiveness of probabilistic tracking of objects in image sequences has been revolutionized by the development of particle filtering. Whereas Kalman filters are restricted to Gaussian distributions, particle filters can propagate more general distributions, albeit only approximately. This is of particular benefit in visual tracking because of the inherent ambiguity of the visual world that s... View full abstract»

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  • Recursive neural filters and dynamical range transformers

    Publication Year: 2004, Page(s):514 - 535
    Cited by:  Papers (3)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (648 KB) | HTML iconHTML

    A recursive neural filter employs a recursive neural network to process a measurement process to estimate a signal process. This paper reviews and presents new results on recursive neural filters through introducing those based on a multilayer perceptron with output feedbacks and proposing dynamical range reducers and extenders for applications where the range of the measurement or signal process ... View full abstract»

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  • Probabilistic data association techniques for target tracking in clutter

    Publication Year: 2004, Page(s):536 - 557
    Cited by:  Papers (79)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (816 KB) | HTML iconHTML

    In tracking targets with less-than-unity probability of detection in the presence of false alarms (FAs), data association-deciding which of the received multiple measurements to use to update each track-is crucial. Most algorithms that make a hard decision on the origin of the true measurement begin to fail as the FA rate increases or with low observable (low probability of target detection) maneu... View full abstract»

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  • Tracking highly maneuverable targets with unknown behavior

    Publication Year: 2004, Page(s):558 - 574
    Cited by:  Papers (7)  |  Patents (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (544 KB) | HTML iconHTML

    Tracking of highly maneuvering targets with unknown behavior is a difficult problem in sequential state estimation. The performance of predictive-model-based Bayesian state estimators deteriorates quickly when their models are no longer accurate or their process noise is large. A data-driven approach to tracking, the segmenting track identifier (STI), is presented as an algorithm that operates wel... View full abstract»

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  • Electrical engineering Hall of Fame-Edwin H. Armstrong

    Publication Year: 2004, Page(s):575 - 578
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (352 KB) | HTML iconHTML

    No abstract available. View full abstract»

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  • Future Special Issues/Special Sections of the IEEE Proceedings

    Publication Year: 2004, Page(s):579 - 580
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  • IEEE Member Digital Library [advertisement]

    Publication Year: 2004, Page(s): c3
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  • Proceedings of the IEEE check out our April issue

    Publication Year: 2004, Page(s): c4
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H. Joel Trussell
North Carolina State University