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

共 426 条记录，以下是51-60 订阅

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A Survey of Imagery Techniques for Semantic Labeling of Human-Vehicle Interactions in Persistent Surveillance Systems

A Survey of Imagery Techniques for Semantic Labeling of Huma...

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

作者： Elangovan, Vinayak Shirkhodaie, Amir Tennessee State Univ Ctr Excellence Battlefield Sensor Fus Dept Mech & Mfg Engn Nashville TN 37203 USA

ISBN: (纸本)9780819486240

Understanding and semantic annotation of Human-Vehicle Interactions (HVI) facilitate fusion of Hard sensor (HS) and Human Intelligence (HUMINT) in a cohesive way. By characterization, classification, and discrimination of HVI patterns pertinent threats may be realized. Various Persistent Surveillance System (PSS) imagery techniques have been proposed in the past decade for identifying human interactions with various objects in the environment. Understanding of such interactions facilitates to discover human intentions and motives. However, without consideration of incidental context, reasoning and analysis of such behavioral activities is a very challenging and difficult task. This paper presents a current survey of related publications in the area of context-based Imagery techniques applied for HVI recognition, in particular, it discusses taxonomy and ontology of HVI and presents a summary of reported robust image processing techniques for spatiotemporal characterization and tracking of human targets in urban environments. The discussed techniques include model-based, shape-based and appearance-based techniques employed for identification and classification of objects. A detailed overview of major past research activities related to HVI in PSS with exploitation of spatiotemporal reasoning techniques applied to semantic labeling of the HVI is also presented.

关键词： Human-Vehicle Interactions (HVI) Semantic Labeling Imagery Data processing sensor Data fusion Persistent Surveillance System (PSS) Spatiotemporal Reasoning Human-Vehicle Taxonomy and Ontology

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Machine learning model cards toward model-based system engineering analysis of resource-limited systems 32

Machine learning model cards toward model-based system engin...

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

作者： Booth, Thomas M. Ghosh, Sudipto USAF 309th SWEG Hill AFB UT 84056 USA Colorado State Univ Ft Collins CO USA

ISBN: (数字)9781510662117

ISBN: (纸本)9781510662100;9781510662117

sensor fusion combines data from a suite of sensors into an integrated solution that represents the target environment more accurately than that produced by individual sensors. New developments in Machine Learning (ML) algorithms are leading to increased accuracy, precision, and reliability in sensor fusion performance. However, these increases are accompanied by increases in system costs. Aircraft sensor systems have limited computing, storage, and bandwidth resources, which must balance monetary, computational, and throughput costs, sensor fusion performance, aircraft safety, data security, robustness, and modularity system objectives while meeting strict timing requirements. Performing trade studies of these system objectives should come before incorporating new ML models into the sensor fusion software. A scalable and automated solution is needed to quickly analyze the effects on the system's objectives of providing additional resources to the new inference models. Given that model-based systems engineering (MBSE) is a focus of the majority of the aerospace industry for designing aircraft mission systems, it follows that leveraging these system models can provide scalability to the system analyses needed. This paper proposes adding empirically derived sensor fusion RNN performance and cost measurement data to machine-readable Model Cards. Furthermore, this paper proposes a scalable and automated sensor fusion system analysis process for ingesting SysML system model information and RNN Model Cards for system analyses. The value of this process is the integration of data analysis and system design that enables rapid enhancements of sensor system development.

关键词： MBSE SysML Machine Learning Model Card sensor fusion Resource datasheets factsheets

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A simple algorithm for sensor fusion using spatial voting (unsupervised object grouping)

A simple algorithm for sensor fusion using spatial voting (u...

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

作者： Jaenisch, Holger M. Albritton, Nathaniel G. Handley, James W. Burnett, Randel B. Caspers, Robert W. Albritton, William P., Jr. Alabama A&M Univ Dept Phys Nanosci Grp Normal AL 35762 USA Licht Strahl Engn INC Toney AL 35773 USA Amtec Corp Huntsville AL 35816 USA

ISBN: (纸本)9780819471598

We present a simple algorithm for achieving unsupervised spatially distributed object fusion using spatial voting. We achieve spatial fusion of uncertain position estimates of disparate objects. These objects are portions of larger assemblies that cannot be directly observed by available sensors. Only the individual objects are discernable. The question arises how to fuse estimates of position uncertainty of these objects (potential assembly pieces) into an "assembly" whose location and extent can only be inferred. Our algorithm uses spatial correlation and stacking with voting of positional uncertainty ellipses. We present the positional algorithm and compare it to other methods of grouping uncertainties and find encouraging improved performance.

关键词： data model sensor fusion spatial voting data fusion clustering K-Means nearest neighbor MLANS

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Retrospectives on the Applications AI and Deep Learning in Information fusion 27

Retrospectives on the Applications AI and Deep Learning in I...

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

作者： Kadar, Ivan Interlink Syst Sci Inc 1979 Marcus Ave Lake Success NY 11042 USA

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Information fusion Designed for (Robust) Action

Information Fusion Designed for (Robust) Action

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

作者： Jones, Eric Tierno, Jorge Syst & Technol Res Woburn MA USA Barnstorm Res Corp Malden MA USA

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Cognitive foundations for model-based sensor fusion

Cognitive foundations for model-based sensor fusion

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

作者： Perlovsky, LI Weijers, B Mutz, CW AFRL SNHE Hanscom AFB MA USA

ISBN: (纸本)0819449563

target detection, tracking, and sensor fusion are complicated problems, which usually are performed sequentially. First detecting targets, then tracking, then fusing multiple sensors reduces computations. This procedure however is inapplicable to difficult targets which cannot be reliably detected using individual sensors, on individual scans or frames. In such more complicated cases one has to perform functions of fusing, tracking, and detecting concurrently. This often has led to prohibitive combinatorial complexity and, as a consequence, to sub-optimal performance as compared to the information-theoretic content of all the available data. It is well appreciated that in this task the human mind is by far superior qualitatively to existing mathematical methods of sensor fusion, however, the human mind is limited in the amount of information and speed of computation it can cope with. Therefore, research efforts have been devoted toward incorporating "biological lessons" into smart algorithms, yet success has been limited. Why is this so, and how to overcome existing limitations? The fundamental reasons for current limitations are analyzed and a potentially breakthrough research and development effort is outlined. We utilize the way our mind combines emotions and concepts in the thinking process and present the mathematical approach to accomplishing this in the current technology computers. The presentation will summarize the difficulties encountered by intelligent systems over the last 50 years related to combinatorial complexity, analyze the fundamental limitations of existing algorithms and neural networks, and relate it to the type of logic underlying the computational structure: formal, multivalued, and fuzzy logic. A new concept of dynamic logic will be introduced along with algorithms capable of pulling together all the available information from multiple sources. This new mathematical technique, like our brain, combines conceptual understanding with emotional

关键词： sensor fusion target tracking dynamic logic neural networks

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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...

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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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Optimal Update with Multiple Out-of-Sequence Measurements

Optimal Update with Multiple Out-of-Sequence Measurements

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

作者： Zhang, Shuo Bar-Shalom, Yaakov Univ Connecticut ECE Dept Storrs CT 06269 USA

ISBN: (纸本)9780819486240

In multisensor target tracking systems receiving out-of-sequence measurements from local sensors is a common situation. In the last decade many algorithms have been proposed to update a target state with an OOSM optimally or suboptimally. However, what one faces in the real world is multiple OOSMs, which arrive at the fusion center in, generally, arbitrary orders, e. g., in succession or interleaved with in-sequence measurements. A straightforward approach to deal with this multi-OOSM problem is by sequentially applying a given OOSM algorithm;however, this simple solution does not guarantee optimal update under the multi-OOSM scenario. The present paper discusses the differences between the single-OOSM processing and the multi-OOSM processing, and presents the general solution to the multi-OOSM problem, called the complete in-sequence information (CISI) approach. Given an OOSM, in addition to updating the target state at the most recent time, the CISI approach also updates the states between the OOSM time and the most recent time, including the state at the OOSM time. Three novel CISI methods are developed in this paper: the information filter-equivalent measurement (IF-EqM) method, the CISI fixed-point smoothing (CISI-FPS) method and the CISI fixed-interval smoothing (CISI-FIS) method. Numerical examples are given to show the optimality of these CISI methods under various multi-OOSM scenarios.

关键词： target tracking centralized fusion time delay OOSM (out-of-sequence measurement)

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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...

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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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fusion and Gaussian Mixture Based Classifiers for SONAR Data

Fusion and Gaussian Mixture Based Classifiers for SONAR Data

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

作者： Kotari, Vikas Chang, K. C. George Mason Univ Dept SEOR Fairfax VA 22030 USA

ISBN: (纸本)9780819486240

Underwater mines are inexpensive and highly effective weapons. They are difficult to detect and classify. Hence detection and classification of underwater mines is essential for the safety of naval vessels. This necessitates a formulation of highly efficient classifiers and detection techniques. Current techniques primarily focus on signals from one source. Data fusion is known to increase the accuracy of detection and classification. In this paper, we formulated a fusion-based classifier and a Gaussian mixture model (GMM) based classifier for classification of underwater mines. The emphasis has been on sound navigation and ranging (SONAR) signals due to their extensive use in current naval operations. The classifiers have been tested on real SONAR data obtained from University of California Irvine (UCI) repository. The performance of both GMM based classifier and fusion based classifier clearly demonstrate their superior classification accuracy over conventional single source cases and validate our approach.

关键词： Data fusion Gaussian Mixture model SONAR detection and classification

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