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检索条件"任意字段=Conference on Signal Processing, Sensor Fusion, and Target Recognition VIII"

共 428 条记录，以下是31-40 订阅

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Dempster-Shafer Theory and Connections to Choquet's Theory of Capacities and Information Theory

Dempster-Shafer Theory and Connections to Choquet's Theory o...

引用

conference on signal processing, sensor/Information fusion, and target recognition XXIII

作者： Peri, Joseph S. J. Johns Hopkins Univ Appl Phys Lab Laurel MD 20723 USA

ISBN: (纸本)9781628410280

The axiomatic development of information theory, during the 1960's, led to the discovery of various composition laws. The Wiener-Shannon law is well understood, but the Inf law holds particular interest because it creates a connection with the Dempster-Shafer theory. Proceeding along these lines, in a previous paper, I demonstrated the connection between the Dempster-Shafer theory and Information theory. In 1954, Gustave Choquet developed the theory of capacities in connection with potential theory. The basic concepts of capacity theory arise from electrostatics, but a capacity is a generalization of the concept of measure in Analysis. It is well known that Belief and Plausibility in the Dempster-Shafer theory are Choquet capacities. However, it is not well known that the inverse of an information measure is a Choquet capacity. The objective of this paper is to demonstrate the connections among the Dempster-Shafer theory, Information theory and Choquet's theory of capacities.

关键词： Information theory sensors Associative arrays Probability theory Applied physics Communication theory Mathematics sensor fusion signal processing target recognition

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Registration of SAR and FLIR images for ATR applications

Registration of SAR and FLIR images for ATR applications

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conference on signal processing, sensor fusion, and target recognition X

作者： Chen, Y HRL Labs LLC Malibu CA 90265 USA

ISBN: (纸本)0819440752

We present an algorithm for SAR and FUR image registration with the goal of facilitating the matching or association of potential targets detected from both images. Our algorithm is a feature point based approach, therefore the preprocessing is relatively simple. We use available sensor truth information to establish an initial registration, which we argue is a good approximation to the actual one up to a linear translation caused by errors in two parameters in the sensor truth. A generalized Hough transform, or GHT, in 2-D is then used to find the residual translational registration error by finding a maximal set of matching feature points from the SAR and FLIR. The registration transformation is then updated using the set of matching feature points found by GHT. Due to the robustness of GHT to noise in the form of uncorrelated clutter feature points, our algorithm can tolerate a significant level of false alarms in the feature detection process, thereby allowing for simple detection processes to be used and still achieve very high overall target detection rate after the registration process.

关键词： image registration feature correspondence sensor fusion automatic target recognition

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Hybrid Probabilistic Information Matrix fusion 30

Hybrid Probabilistic Information Matrix Fusion

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conference on signal processing, sensor/Information fusion, and target recognition XXX

作者： Yang, Kaipei Bar-Shalom, Yaakov Hunt, Shawn Univ Connecticut Dept ECE Storrs CT 06269 USA DENSO Int Amer Inc Storrs CT USA

ISBN: (数字)9781510643505

ISBN: (纸本)9781510643505

In autonomous driving systems, distributed sensor fusion is widely used where each sensor has its tracking system and only the local tracks (LT) are transmitted to the fusion center. We consider the fusion of LTs taking into account all the fusion Center (FC) track-to-LT association hypotheses via probabilities in the proposed Hybrid Probabilistic Information Matrix fusion (HPIMF) algorithm. In HPIMF, the track association and fusion are carried out with probabilistic weightings rather than using a single track association only. Different from track-to-track fusion (T2TF), which is one of the most commonly used approaches for distributed tracking systems, the associations considered in HPIMF are between the predicted FC state and the LTs from local sensors. At each time for an association event, up to one of the tracks within the track list of a local sensor can be associated with the FC state. In real world scenarios there can be large uncertainties and missed tracks due to sensor imperfections and sensor-target geometry. Consequently, the association might be unreliable and fusion based on only a single association hypothesis could fail. It is shown in the simulations of a realistic autonomous driving system that HPIMF can successfully track a target of interest and is superior to T2TF which relies on hard association decisions.

关键词： Information fusion sensors Distributed computing Detection and tracking algorithms sensor fusion

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Asynchronous sensor fusion for multiple object tag-less activity tracking in manufacturing 33

Asynchronous sensor fusion for multiple object tag-less acti...

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conference on signal processing, sensor/Information fusion, and target recognition XXXIII

作者： Yang, Kaipei Pezeshk, Aria Lehman, Charles Arbabian, Amin Plato Syst 360 Pine St Floor 6 San Francisco CA 94104 USA

ISBN: (纸本)9781510674332;9781510674325

Centralized fusion ensures minimal information loss and maximizes the effectiveness of state estimation. Statistically, it is the optimal solution for all sensor fusion configurations. In this paper, we introduce a local-sensor-driven asynchronous low-level centralized fusion methodology that seamlessly integrates radar and camera data at the level of detections from each sensor. For a local-sensor-driven asynchronous system, detections from the two sensing modalities with different sampling rates are transmitted to a centralized filter, which is updated whenever it receives a measurement. We implemented the proposed algorithm and validated the results using real data from manufacturing and industrial work sites. The data was obtained by Plato System's Argus perception system, which combines high-resolution imaging mm-wave radar with camera sensors to provide indoor and outdoor activity tracking. We further compare the fusion results with vision-only MOT, as well as track-level fusion (track-to-track fusion).

关键词： Centralized fusion Multiple Object Tracking sensor fusion Manufacturing Activity Tracking

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sensor fusion for media manipulation with ambiguous hypotheses, evidence alignment, and a novel belief combination rule 31

Sensor fusion for media manipulation with ambiguous hypothes...

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conference on signal processing, sensor/Information fusion, and target recognition XXXI

作者： Huben, Robert Poppelreiter, Nathan Palladino, Anthony Boston Fus Corp 70 Westview StSuite 100 Lexington MA 02421 USA

ISBN: (数字)9781510651210

ISBN: (纸本)9781510651210;9781510651203

Fusing sensors that each test one hypothesis from a set of ambiguous, non-exclusive hypotheses about manipulation in media using only combination rules like those in Dempster-Shafer fusion ignores information about the overlap of the sensors' beliefs, hypotheses, and evidences. We present a novel fusion approach for sensors that test ambiguous hypotheses using semantic evidence. Our approach measures the relevance of sensors to a given hypothesis and leverages the ambiguity of hypotheses and the similarity of evidences across sensors. These factors lead to a combination rule that captures the conceptual overlaps among hypotheses, evidences, and sensors.

关键词： sensor fusion media manipulation hypotheses non-exclusive hypotheses evidence alignment

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target location and sensor fusion through calculated and measured image differencing

Target location and sensor fusion through calculated and mea...

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conference on signal processing, sensor fusion, and target recognition XII

作者： Baer, W Campbell, TR USN Postgrad Sch Dept Informat Sci Monterey CA 93943 USA

ISBN: (纸本)0819449563

The ability to rapidly detect and identify potential targets both fixed and mobile from multiple sensor feeds is a critical function in network centric warfare. In this paper we describe the use of Image Differencing and 3D terrain database editing in order to fuse oblique aerial photos, IR sensor imagery, and other non-traditional data sources to produce battlefield metrics that support network centric operations. Such metrics include target detection, recognition, and location, and improved knowledge of the target environment. Key to our approach is the rapid generation of target and background signatures from high-resolution 1-meter object descriptor terrain databases. This technique utilizes the difference between measured and calculated sensor images to 1) update and correct knowledge of the terrain background, 2) register multi sensor imagery 3) identify potential/candidate targets based on residual image differencing and 3) measure and report target locations based on scene matching. The technique is especially suited for utilizing imagery from reconnaissance and remotely piloted vehicle sensors. It also holds promise for automation and real-time data reduction of battlefield sensor feeds and for improving now-time situational awareness. We will present the algorithms and approach utilized in the Image Differencing technique. We will also describe the software developed to implement the approach. Lastly we will present the results of experiments and benchmarks conducted to identify and measure target locations in test locations at Ft. Hood, TX and Ft. Hunter Liggett, CA.

关键词： remote sensing target mensuration image feedback image differencing sensor fusion time critical targeting

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Multitarget tracking using multiple bistatic range measurements with probability hypothesis densities

Multitarget tracking using multiple bistatic range measureme...

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conference on signal processing, sensor fusion, and target recognition XIII

作者： Tobias, M Lanterman, AD Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30332 USA

ISBN: (纸本)0819453528

Ronald Mahler's Probability Hypothesis Density (PHD) provides a promising framework for the passive coherent location of targets observed via multiple bistatic radar measurements. We consider tracking targets using only range measurements from a simple non-directional receiver that exploits non-cooperative FM radio transmitters as its "illuminators of opportunity." A target cannot be located at a single point by a particular transmitter-receiver pair, but rather it is located along a bistatic range ellipse determined by the position of the target relative to the receiver and transmitter. target location is resolved by using multiple transmitter-receiver pairs and locating the target at the intersection of the resulting bistatic ellipses. Determining the intersection of these bistatic range ellipses and resolving the resultant ghost targets is generally a complex task. However, the PHD provides a convenient and simple means of fusing together the multiple range measurements to locate targets. We incorporate signal-to-noise ratios, probabilities of detection and false alarm, and bistatic range variances into our simulation.

关键词： multitarget tracking probability hypothesis density passive radar sensor fusion passive coherent location

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Nonlinear operator for fusion of IR and radar data

Nonlinear operator for fusion of IR and radar data

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conference on signal processing, sensor fusion, and target recognition viii

作者： Birkemark, CM Titley, JD Danish Def Res Estab DK-2100 Copenhagen 0 Denmark

ISBN: (纸本)081943194X

A pre-detection nonlinear operator of the "soft decision type" is proposed for the fusion of IR and radar data. The proposed operator fuses data prior to information extraction, with the advantage that no information is considered redundant prior to fusion. This we define as the "Derived Radar and Infra-Red Voxel Energy" or the DRIVE operator. The signals arise from independent stochastic processes, and are nominally uncorrelated. The behavior of the expectation of the fusion response describes the data with respect to the application of detection of the target. The above hypothesis is being investigated through fully controlled experiments where data are simultaneously collected with the Danish Defence Research Establishment (DDRE) Modular Radar System (MRS) high range resolution radar and a commercially available IR camera. Focus is on the region where data are marginal;that is, where neither radar data nor IR data alone gives an extractable signal. The theoretical and experimental results are discussed.

关键词： multisensor data fusion nonlinear operator IR and radar pre-detection

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An Analytic Solution to Ellipsoid Intersections for Multistatic Radar 27

An Analytic Solution to Ellipsoid Intersections for Multista...

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conference on signal processing, sensor/Information fusion, and target recognition XXVII

作者： Shapero, Samuel A. Georgia Tech Res Inst 925 Dalney St NW Atlanta GA 30332 USA

ISBN: (数字)9781510618046

ISBN: (纸本)9781510618046

Unlike monostatic radars that directly measure the range to a target, multistatic radars measure the total path length from a transmitter, to the target, and then to the receiver. In the absence of angle information, the region of uncertainty described by such a measurement is the surface of an ellipsoid. In order to precisely locate the target, at least three such measurements are needed. In this paper, we derive from geometrical methods a general algorithmic solution to the intersection of three ellipsoids with a common focus. Applying the solution to noisy measurements via the cubature rule provides a solution that approaches the Cramer Rao Lower Bound, which we demonstrate via Monte-Carlo analysis. For conditions of low noise with non-degenerate geometries we also provide a consistent covariance estimate.

关键词： Multistatic radar passive bistatic radar nonlinear estimation sensor fusion

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Practical information-based data fusion performance evaluation

Practical information-based data fusion performance evaluati...

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conference on signal processing, sensor fusion, and target recognition viii

作者： Zajic, T Mahler, R Lockheed Martin Tact Def Syst Eagan MN 55121 USA

ISBN: (纸本)081943194X

The ability to meaningfully assess the competence of algorithms is a crucial part of developing and comparing practical systems. Moreover, the importance of metrology has increased because of the emergence of fusion strategies such as adaptive fusion and fusion management, which require that Measures of Performance (MoPs), Effectiveness (MoEs), and Robustness (MoRs) be examined with greater seriousness. It would seem, therefore, that few things could be as important as achieving a scientific understanding of measurement. In reality, probably no other vital aspect of multisource-multisensor data fusion has been less glamorous, more heuristic, more poorly understood, and less a subject of deep examination than has been metrology. In this paper we present preliminary findings of an ongoing project on scientific performance evaluation for multisource-multisensor data fusion, sponsored by Air Force Research Laboratories, Rome NY.

关键词： performance evaluation information metrics adaptive data fusion random sets

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