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Information Fusion, 2009. FUSION '09. 12th International Conference on

Date 6-9 July 2009

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  • Welcome

    Publication Year: 2009, Page(s): i
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  • The 12th International conference on information fusion

    Publication Year: 2009, Page(s): 1
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  • Schedule of events

    Publication Year: 2009, Page(s):2 - 3
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  • Welcome message

    Publication Year: 2009, Page(s):4 - 6
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  • The real world

    Publication Year: 2009, Page(s): 10
    Cited by:  Papers (2)
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  • The Urban Challenge

    Publication Year: 2009, Page(s): 10
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  • Likelihood Ratio Detection and Tracking

    Publication Year: 2009, Page(s): 10
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  • Tutorials

    Publication Year: 2009, Page(s): 11
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (75 KB)

    Provides an abstract for each of the tutorial presentations and a brief professional biography of each presenter. The complete presentations were not made available for publication as part of the conference proceedings. View full abstract»

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  • Technical program

    Publication Year: 2009, Page(s):12 - 25
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  • Social program

    Publication Year: 2009, Page(s):26 - 27
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  • General information

    Publication Year: 2009, Page(s):30 - 31
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  • Organizing Committee

    Publication Year: 2009, Page(s): 7
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  • Technical Program Committee

    Publication Year: 2009, Page(s):8 - 9
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  • Sponsors

    Publication Year: 2009, Page(s):28 - 29
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  • Table of contents

    Publication Year: 2009, Page(s):1 - 27
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  • Performance evaluation of MHT and GM-CPHD in a ground target tracking scenario

    Publication Year: 2009, Page(s):300 - 307
    Cited by:  Papers (3)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (940 KB) | HTML iconHTML

    Performance evaluations of multi-target tracking algorithms are often limited to consider comparisons within the same algorithm family. In this paper, two conceptually different multi-target tracking algorithms are evaluated, namely a multiple-hypothesis tracking (MHT) algorithm and the Gaussian mixture cardinalized probability hypothesis density (GM-CPHD) filter. As a reference, a conventional si... View full abstract»

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  • Improving self-alignment of strapdown INS using measurement augmentation

    Publication Year: 2009, Page(s):1783 - 1789
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (397 KB) | HTML iconHTML

    An alternative solution to the velocity based self alignment of strapdown inertial navigation system is proposed. It is well known that a simple Kalman filter based solution to the above problem fails to provide accurate azimuth alignment due to the inherent lack of observability of the model in the presence of instrument bias. Earlier researchers use external digital filters to obtain improved es... View full abstract»

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  • Track-Before-Detect for sensors with complex measurements

    Publication Year: 2009, Page(s):618 - 625
    Cited by:  Papers (1)
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (1130 KB) | HTML iconHTML

    Track before detect (TkBD) is a paradigm that combines the target detection and estimation processes that are usually sequentially applied to sensor data in a conventional system. Under TkBD the single frame detector is removed and the tracker is supplied with the whole sensor image. Detection decisions are then shifted to the output of the tracker which is able to use temporal correlation to impr... View full abstract»

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  • The track repulsion effect in automatic tracking

    Publication Year: 2009, Page(s):2225 - 2230
    Cited by:  Papers (2)
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (442 KB) | HTML iconHTML

    The track coalescence effect degrades the performance of probabilistic data association trackers in dense target scenarios. Recently, it has been observed that an opposite effect exists with trackers that utilize hard data association, which we denote as the track repulsion effect. In this paper, we examine this effect in the context of a crossing target scenario, and explore the effectiveness of ... View full abstract»

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  • Multi-stage data fusion and the MSTWG TNO datasets

    Publication Year: 2009, Page(s):1552 - 1559
    Cited by:  Papers (1)
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (1551 KB) | HTML iconHTML

    Data fusion is a branch of applied science that processes measurements from a variety of sources and time epochs to provide a consolidated state history of a reality of interest, be it a physical process, an intangible system (e.g. economic, social, ...), or a complex entity that includes elements of both (e.g. a set of individuals in physical space). As such, data fusion is an important component... View full abstract»

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  • Suboptimal JPDA for tracking in the presence of clutter and missed detections

    Publication Year: 2009, Page(s):818 - 825
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1218 KB) | HTML iconHTML

    A new heuristic for data association on multi-target tracking systems is presented. The algorithm is based on the existing JPDA (Joint Probabilistic Data Association) algorithm, more specifically, on the Suboptimal JPDA approximation. However, compared to Suboptimal JPDA, the presented method exhibits a significant improvement on tracking performance for scenarios in the presence of clutter and mi... View full abstract»

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  • Improvement in track-to-track association from using an adaptive threshold

    Publication Year: 2009, Page(s):1627 - 1633
    Cited by:  Papers (1)
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (372 KB) | HTML iconHTML

    This paper considers the performance of two track-to-track association algorithms. The first bases association decisions on a chi-squared distance between tracks and a fixed significance threshold to determine when no association is allowed. The second algorithm finds the maximum a posteriori probability (MAP) set of associations between tracks from two independent tracking systems. For tracks who... View full abstract»

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  • A new nonlinear filtering method for ballistic target tracking

    Publication Year: 2009, Page(s):2062 - 2067
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (343 KB) | HTML iconHTML

    Tracking a ballistic re-entry target from radar observations is a highly complex problem in nonlinear filtering. The paper adopts a one-dimensional vertical motion model with unknown ballistic coefficient, we present a square-root quadrature Kalman filter (SRQKF) algorithm for this ballistic target tracking problem. The proposed algorithm is the square-root implementation of the quadrature Kalman ... View full abstract»

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  • Acoustic vehicle classification by fusing with semantic annotation

    Publication Year: 2009, Page(s):232 - 239
    Cited by:  Papers (1)
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (2005 KB) | HTML iconHTML

    Current research on acoustic vehicle classification has been generally aimed at utilizing various feature extraction methods and pattern recognition techniques. Previous research in gait biometrics has shown that domain knowledge or semantic enrichment can assist in improving the classification accuracy. In this paper, we address the problem of semantic enrichment by learning the semantic attribut... View full abstract»

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  • Threat assessment using context-based tracking in a maritime environment

    Publication Year: 2009, Page(s):187 - 194
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (1603 KB) | HTML iconHTML

    The main objective of this work is to model and exploit available maritime contextual information to provide a hypothesis on suspicious vessel maneuvers. This concept involves utilizing the L1 tracking to perform L2/L3 data fusion, i.e. refinement and assessment for situations and threats. A new context-based tracker known as the ConTracker is developed. The purpose of the ConTracker is to incorpo... View full abstract»

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