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

共 426 条记录，以下是61-70 订阅

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High Level Information fusion (HLIF) with Nested fusion Loops

High Level Information Fusion (HLIF) with Nested Fusion Loop...

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

作者： Woodley, Robert Gosnell, Michael Fisher, Amber 21st Century Syst Inc Omaha NE 68106 USA

ISBN: (纸本)9780819495365

Situation modeling and threat prediction require higher levels of data fusion in order to provide actionable information. Beyond the sensor data and sources the analyst has access to, the use of out-sourced and re-sourced data is becoming common. Through the years, some common frameworks have emerged for dealing with information fusion-perhaps the most ubiquitous being the JDL Data fusion Group and their initial 4-level data fusion model. Since these initial developments, numerous models of information fusion have emerged, hoping to better capture the human-centric process of data analyses within a machine-centric framework. 21st Century Systems, Inc. has developed fusion with Uncertainty Reasoning using Nested Assessment Characterizer Elements (FURNACE) to address challenges of high level information fusion and handle bias, ambiguity, and uncertainty (BAU) for Situation Modeling, Threat Modeling, and Threat Prediction. It combines JDL fusion levels with nested fusion loops and state-of-the-art data reasoning. Initial research has shown that FURNACE is able to reduce BAU and improve the fusion process by allowing high level information fusion (HLIF) to affect lower levels without the double counting of information or other biasing issues. The initial FURNACE project was focused on the underlying algorithms to produce a fusion system able to handle BAU and repurposed data in a cohesive manner. FURNACE supports analyst's efforts to develop situation models, threat models, and threat predictions to increase situational awareness of the battlespace. FURNACE will not only revolutionize the military intelligence realm, but also benefit the larger homeland defense, law enforcement, and business intelligence markets.

关键词： High Level Information fusion (HLIF) Knowledge Discovery Nested fusion Loops Situation Assessment and Modeling Threat and Impact Assessment Data Reuse Data Analysis Bias and Ambiguity and Uncertainty Handling

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Filter Design for Tracking of Ballistic target Missile using Seeker Measurements with Time Lag

Filter Design for Tracking of Ballistic Target Missile using...

引用

International conference on signal processing, Image processing and Pattern recognition (ICSIPR)

作者： Mukherjee, Arpita Mukherjee, Debaprasad Sengupta, Aparajita CSIR Cent Mech Engn Res Inst Elect & Instrumentat Lab Durgapur 713209 India West Bengal Univ Technol Dr BC Roy Engn Coll Dept Informat Technol Durgapur 713206 India Bengal Engn & Sci Univ Dept Elect Engn Howrah 711103 India

ISBN: (纸本)9781467348621;9781467348614

Here a method has been proposed to estimate the realtime state using the sensor measurement which contains lag due the mechanism of data collection and transmission to the estimator for real time state estimation. The error due to the measurement lag can be handle by incorporating the sensor dynamics in the system model. In this paper A extended Kalman filter has been designed to estimate relative kinematic of a ballistic target missile w.r.t. interceptor missile using noisy and lagged seeker measurements in exo atmospheric region. The seeker dynamics has been modeled and included in the system model of the estimator design to handle lag present in the seeker measurement due to processing and transmitting data which are obtained from different reference frame.

关键词： Kalman filters aerospace engineering military computing missiles nonlinear filters sensor fusion target tracking

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High-resolution land cover classification using low-resolution global map data

High-resolution land cover classification using low-resoluti...

引用

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

作者： Carlotto, Mark J. General Dynamics Advanced Information Systems (United States)

ISBN: (纸本)9780819495365

A fusion approach is described that combines texture features from high-resolution panchromatic imagery with land cover statistics derived from co-registered low-resolution global databases to obtain high-resolution land cover maps. The method does not require training data or any human intervention. We use an MxN Gabor filter bank consisting of M=16 oriented bandpass filters (0-180 degrees) at N resolutions (3-24 meters/pixel). The size range of these spatial filters is consistent with the typical scale of manmade objects and patterns of cultural activity in imagery. Clustering reduces the complexity of the data by combining pixels that have similar texture into clusters (regions). Texture classification assigns a vector of class likelihoods to each cluster based on its textural properties. Classification is unsupervised and accomplished using a bank of texture anomaly detectors. Class likelihoods are modulated by land cover statistics derived from lower resolution global data over the scene. Preliminary results from a number of Quickbird scenes show our approach is able to classify general land cover features such as roads, built up area, forests, open areas, and bodies of water over a wide range of scenes.

关键词： Gabor filters land cover classification texture features image-map data fusion

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EO and radar fusion for fine-grained target classification with a strong few-shot learning baseline 33

EO and radar fusion for fine-grained target classification w...

引用

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

作者： Ballan, Luca Melo, Jorge G. O. van den Broek, Sebastiaan P. Baan, Jan Heslinga, Friso G. Huizinga, Wyke Dijk, Judith Dilo, Arta TNO Intelligent Imaging Oude Waalsdorperweg 63 NL-2597AK The Hague Netherlands

ISBN: (纸本)9781510674332;9781510674325

Combining data from multiple sensors to improve the overall robustness and reliability of a classification system has become crucial in many applications, from military surveillance and decision support, to autonomous driving, robotics, and medical imaging. This so-called sensor fusion is especially interesting for fine-grained target classification, in which very specific sub-categories (e.g. ship types) need to be distinguished, a task that can be challenging with data from a single modality. Typical modalities are electro-optical (EO) image sensors, that can provide rich visual details of an object of interest, and radar, that can yield additional spatial information. Defined by the approach used to combine data from these sensors, several fusion techniques exist. For example, late fusion can merge class probabilities outputted by separate processing pipelines dedicated to each of the individual sensor data. In particular, deep learning (DL) has been widely leveraged for EO image analysis, but typically requires a lot of data to adapt to the nuances of a fine-grained classification task. Recent advances in DL on foundation models have shown a high potential when dealing with in-domain data scarcity, especially in combination with few-shot learning. This paper presents a framework to effectively combine EO and radar sensor data, and shows how this method outperforms stand-alone single sensor methods for fine- grained target classification. We adopt a strong few-shot image classification baseline based on foundation models, which robustly handles the lack of in-domain data and exploits rich visual features. In addition, we investigate a weighted and a Bayesian fusion approach to combine target class probabilities outputted by the image classification model and radar kinematic features. Experiments with data acquired in a measurement campaign at the port of Rotterdam show that our fusion method improves on the classification performance of individual modalities.

关键词： deep learning sensor fusion image classification few-shot learning foundation models EO and radar decision support

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Hierarchical tracking for the real world

Hierarchical tracking for the real world

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

作者： Bar-Shalom, Y Univ Connecticut ECE Dept Storrs CT 06269 USA

This report describes several information processing configurations for hierarchical tracking, their performances and discusses the selection of one of them.

ISBN: (纸本)0819453528

This report describes several information processing configurations for hierarchical tracking, their performances and discusses the selection of one of them.

关键词： hierarchical tracking fusion

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Combining Structured Light and Ladar for Pose Tracking in THz sensor Management

Combining Structured Light and Ladar for Pose Tracking in TH...

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

作者： Engstrom, Philip Axelsson, Maria Karlsson, Mikael Swedish Def Res Agcy FOI Linkoping Sweden

ISBN: (纸本)9780819495365

Stand-off 3D THz imaging to detect concealed treats is currently under development. The technology can provide high resolution 3D range data of a passing subject showing layers of clothes and if there are concealed items. However, because it is a scanning sensor technology with a narrow field of view, the subjects pose and position need to be accurately tracked in real time to focus the system and map the imaged THz data to specific body parts. Structured light is a technique to obtain 3D range information. It is, for example, used in the Microsoft Kinect for pose tracking of game players in real time. We demonstrate how structured light can contribute to a THz sensor management system and track subjects in real time. The main advantage of structured light is its simplicity, the disadvantages are the sensitivity to lighting conditions and material properties as well as a relatively low accuracy. Time of flight laser scanning is a technique that complements structured light well, the accuracy is usually much higher and it is less sensitive to lighting conditions. We show that by combining the techniques it is possible to create a robust real time pose tracking system for THz sensor management. We present a concept system based on the Microsoft Kinect and a SICK LMS-511 laser scanner. The laser scanner is used for 2D tracking of the subjects, this tracking is then used to initialize and validate the Microsoft Kinect pose tracking. We have evaluated the sensors individually in both static and dynamic scenes and present their advantages and drawbacks.

关键词： Kinect Ladar THz Scanning fusion Pose Tracking Motion Capture

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Collaborative distributed data fusion architecture using multi-level Markov decision processes

Collaborative distributed data fusion architecture using mul...

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

作者： Akselrod, D. Sinha, A. Kirubarajan, T. McMaster Univ Dept Elect & Comp Engn Hamilton ON Canada

No abstract available

ISBN: (纸本)9780819466891

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Research challenges: Dependence issues in feature/declaration data fusion - art. no. 65670N

Research challenges: Dependence issues in feature/declaratio...

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

作者： Kadar, Ivan Interlink Syst Sci Inc Lake Success NY 11042 USA

No abstract available

ISBN: (纸本)9780819466891

No abstract available

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fusion of multispectral and stereo information for unsupervised target detection in VHR airborne data

Fusion of multispectral and stereo information for unsupervi...

引用

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

作者： Borghys, Dirk C. Idrissa, Mahamadou Shimoni, Michal Friman, Ola Axelsson, Maria Lundberg, Mikael Perneel, Christiaan Royal Mil Acad Dept CISS Brussels Belgium Swedish Def Res Agy Linkoping Sweden Royal Mil Acad Dept Math Brussels Belgium

ISBN: (纸本)9780819495365

Very high resolution multispectral imaging reached a high level of reliability and accuracy for target detection and classification. However, in an urban scene, the complexity is raised, making the detection and the identification of small objects difficult. One way to overcome this difficulty is to combine spectral information with 3D data. A set of (very high resolution) airborne multispectral image sequences was acquired over the urban area of Zeebrugge, Belgium. The data consist of three bands in the visible (VIS) region, one band in the near infrared (NIR) range and two bands in the mid-wave infrared (MWIR) region. Images are obtained images at a frame rate of 1/2 frame per second for the VIS and NIR image and 2 frames per second for the MWIR bands. The sensors have a decimetric spatial resolution. The combination of frame rate with flight altitude and speed results in a large overlap between successive images. The current paper proposes a scheme to combine 3D information from along-track stereo, exploiting the overlap between images on one hand and spectral information on the other hand for unsupervised detection of targets. For the extraction of 3D information, the disparity map between different image pairs is determined automatically using an MRF-based method. For the unsupervised target detection, an anomaly detection algorithm is applied. Different methods for inserting the obtained 3D information into the target detection scheme are discussed.

关键词： target detection people detection vehicle detection aerial images stereo processing multispectral data

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Probabilistic models for context in fusion 24

Probabilistic models for context in fusion

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

作者： Chong, Chee-Yee POB 4082 Los Altos CA 94024 USA

Context-based fusion can be represented by a probabilistic model that contains both situation and context data, as well as conditional probabilities for the random variables. Context management selects context variabl... 详细信息

ISBN: (纸本)9781628415902

关键词： fusion context probabilistic model context management

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