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

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Multispectral low light level image fusion technique

Multispectral low light level image fusion technique

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3rd International conference on signal processing (ICSP 96)

作者： Hu, JH Bai, LF Kong, J Liu, B Wang, LP Zhang, BM School of Electronic Engineering & Optoclectronic Technique Nanjing University of Science and Technology of PLA Nanjing China

ISBN: (纸本)7505338900

Multispectral image fusion is a kind of process of synthesis and processing for multispectral image data. On the basis of optoelectronic imaging technique, the imaging process & feature of low light level(LLL) TV system are analyzed & the principle and processing construction of multispectral LLL TV image fusion are discussed. The proper combination of low light level camera and optical filters is used to implement multispectral image fusion experiment at night for outdoor static scene by the aid of computer. The results of experiment indicate that multispectral low light level image fusion has improved the capability for recognizing targets.

关键词： spectral analysis sensor fusion image processing optical filters television cameras military equipment image sensors television applications multispectral low light level image fusion image synthesis image processing multispectral image data optoelectronic imaging technique imaging process low light level TV system low light level camera optical filters multispectral image fusion experiment night vision outdoor static scene target recognition military reconnaissance

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A Framework for Adaptive MaxEnt Modeling Within Distributed sensors and Decision fusion For Robust target Detection/recognition 27

A Framework for Adaptive <i>MaxEnt</i> Modeling Within Distr...

引用

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

ISBN: (数字)9781510618046

ISBN: (纸本)9781510618046

The Maximum Entropy (MaxEnt) information theoretic model parametric framework was introduced in a prior paper for distributed decision fusion (DDF) without knowledge of prior probabilities of local decisions. The paper demonstrated the effectiveness of the MaxEnt fusion center by achieving the best, realistic detection performance with respect to published results of either the Bayesian formulation or the Neyman-Pearson criterion. This paper represents the framework of an extension of MaxEnt DDF, called E-MaxEnt using: individual sensor MaxEnt classifiers for targets classification/recognition, and by fusing local classifier decisions. Specifically, in E-MaxEnt each sensor has a front-end pre-processing system for both signal detection and to process unique target attributes extracted for example from observed target imagery, which attributes are stored for reference/learning/comparison in the sensors MaxEnt classifiers. Based on the degree of match, each sensor generates local binary decisions that are sent to a MaxEnt fusion center, in the usual parallel architecture. No assumptions are made about knowing any local decision rules. The sensors are taking simultaneous (synchronized) measurements with overlapping FOV overages. It should be noted that the above description is not meant to address the "needle-in-haystack" problem, but rather address finding the presence, viz., classify/recognize a previously seen "known" target in areas where previously seen targets most likely are, along with other targets. At the time of writing, the data sets to test the algorithm were not available, but front-end image processing and MaxEnt classifiers were implemented. It is hoped that someone could provide the necessary data sets so the efficacy of the method could be demonstrated and compared with alternative approaches.

关键词： distributed decision fusion supervised classification learning MaxEnt decision fusion MaxEnt classifiers targets presence detection classification/recognition context sensing

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Cognitive-based fusion using information sets for moving target recognition

Cognitive-based fusion using information sets for moving tar...

引用

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

作者： Blasch, E Watamaniuk, S Svenmark, P Wright State Univ Dept Psychol Dayton OH 45435 USA

ISBN: (纸本)081943678X

Leveraging human fusion can enhance computational moving target recognition algorithms. Cognitive models exploit a human's visual discrimination of object color, size, motion, and orientation. From the biological pathways of the magnocellular (motion, orientation) and parvocellular (color, size) pathways, information sets are fused for a single perception of an object. For instance, a human tracking a target could take advantage of a moving target relative to stationary objects or a large object amongst smaller objects. Cognition, or attention to salient information, can be explicitly represented as a set of information outside a covariance boundary. The paper proposes a cognitive-based attentional model that leverages information asymmetries for moving target recognition.

关键词： ATR attention discrimination asymmetry

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Wide-Area Video Exploitation (WAVE) Joint Data Management (JDM) for Layered Sensing

Wide-Area Video Exploitation (WAVE) Joint Data Management (J...

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

作者： Blasch, Erik P. Seetharaman, Guna Russell, Stephen Def R&D Canada Valcartier 2459 Pie XI Blvd N Quebec City PQ Canada Air Force Res Lab Rome NY 13441 USA Naval Res Lab Washington DC 20375 USA

ISBN: (纸本)9780819486240

Emerging technologies of high performance computing facilitate increased data collection for wide area sensing;however, joint data management concepts of operations (CONOPs) are important to fully realize system-level performance. Joint data management (JDM) includes the hardware (e. g. sensors/targets), software (e. g. processing/algorithms), entities (e. g. service-based collections), and operations (scenario-based environments) of data exchange that enable persistent surveillance in the context of a layered sensing or data-to-decision (D2D) information fusion enterprise. Key attributes of an information fusion enterprise system require pragmatic assessment of data and information management, distributed communications, knowledge representation as well as a sensor mix, algorithm choice, life-cycle data management, and human-systems interaction. In this paper, we explore the various issues surrounding Wide-Area Video Exploitation (WAVE) in a layered-sensing environment to include improvements in Joint Data Management such as (1) data collection, construction, and transformation, (2) feature generation, extraction and selection, and (3) information evaluation, presentation, and dissemination. Throughout the paper, we seek to describe the current technology, research directions, and metrics that instantiate a realizable JDM product. We develop the methods for joint data management for structured and unstructured WAVE data in the context of decision making. Discerning accurate track and identification target information from WAVE JDM provides a moving intelligence (MOVINT) capability.

关键词： Information fusion Situational Assessment sensor planning target tracking data-to-decisions (D2D)

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A Bayesian Tracker for Multi-sensor Passive Narrowband fusion 25

A Bayesian Tracker for Multi-Sensor Passive Narrowband Fusio...

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

作者： Pirkl, Ryan J. Aughenbaugh, Jason M. Univ Texas Austin Appl Res Labs Austin TX 78713 USA

ISBN: (纸本)9781510600836

We demonstrate the detection and localization performance of a multi-sensor, passive sonar Bayesian tracker for underwater targets emitting narrowband signals in the presence of realistic underwater ambient noise. Our evaluation focuses on recent advances in the formulation of the likelihood function used by the tracker that provide greater robustness in the presence of both realistic environmental noise and imprecise/inaccurate a priori knowledge of the target's narrowband signal. These improvements enable the tracker to reliably detect and localize narrowband emitters for a broader range of propagation environments, target velocities, and inherent uncertainty in a priori knowledge.

关键词： Bayesian tracker passive sonar narrowband

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Visualization of Graphical Information fusion Results

Visualization of Graphical Information Fusion Results

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

作者： Blasch, Erik Lechuck, Georgiy Staskevich, Gennady Burke, Dustin Aved, Alex Air Force Res Lab Informat Directorate Rome NY 13441 USA Aptima Woburn MA 01801 USA

ISBN: (纸本)9781628410280

Graphical fusion methods are popular to describe distributed sensor applications such as target tracking and pattern recognition. Additional graphical methods include network analysis for social, communications, and sensor management. With the growing availability of various data modalities, graphical fusion methods are widely used to combine data from multiple sensors and modalities. To better understand the usefulness of graph fusion approaches, we address visualization to increase user comprehension of multi-modal data. The paper demonstrates a use case that combines graphs from text reports and target tracks to associate events and activities of interest visualization for testing Measures of Performance (MOP) and Measures of Effectiveness (MOE). The analysis includes the presentation of the separate graphs and then graph-fusion visualization for linking network graphs for tracking and classification.

关键词： Graphical Information fusion Metrics Analysis

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Track Stitching and Approximate Track Association on a Pairwise-Likelihood Graph 27

Track Stitching and Approximate Track Association on a Pairw...

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

作者： Chen, Lingji Rumbley, Sarah E. NuTonomy Boston MA USA BAE Syst Burlington MA USA

ISBN: (数字)9781510618046

ISBN: (纸本)9781510618046

Single-sensor track stitching is a path cover problem on a graph with pairwise log likelihoods. This paper provides a theoretical justification for pursuing track association on such a graph by using a sum of pairwise log likelihoods in place of the multi-sensor log likelihood. It outlines solution strategies through clique cover, cotemporal subgraph decomposition, and super-node stitching.

关键词： track stitching track association track fusion graph based tracking likelihood multi-target multi-sensor

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Distributed sensor fusion with network constraints

Distributed sensor fusion with network constraints

引用

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

作者： Wang, XZ Evans, R Legg, J Univ Melbourne CRC Sensor Signal & Informat Proc Dept Elect & Elect Engn Melbourne Vic 3010 Australia

ISBN: (纸本)0819453528

In a practical multi-sensor tracking network, the sensor data processors communicate only a subset of the data available from each sensor, usually in the form of tracks due to constraints in the communications bandwidth. This paper investigates source coding methods by which track information may be communicated with varying levels of available bandwidth with the aim of minimizing the number of bits required by the fusion center for a given estimation error. In particular, we have formulated the distributed sensor fusion problem subject to network constraints as a minimization problem in terms of the Kullback-Leibler distance (KLD). We have shown that for the multi-sensor track fusion problem, the global KLD will be minimized when the individual, local KLDs are minimized. The solutions to some special cases are derived and the losses in the accuracy of the target state estimates that result from the process of source coding and subsequent interpretation is also determined. Simulated results demonstrate the consistency between both theoretical and practical results.

关键词： Kullback Leibler Distance (KLD) distributed sensor fusion network (DSFN) source coding network constraints digital data link

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fusion of imaging data and auxiliary signal for target classification

Fusion of imaging data and auxiliary signal for target class...

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

作者： Zatezalo, Aleksandar Yu, Ssu-Hsin Sci Syst Co Inc Woburn MA USA

ISBN: (纸本)9781628410280

fusion of imaging data with auxiliary signal such as EW data for multitarget classification poses daunting theoretical and practical challenges. The problem is exacerbated by issues such as asynchronous data flow, uneven feature quality and object occlusion. In our approach, we assign prior probabilities to image and signal feature elements to handle those practical issues in a unified manner. Current state and class probability distributions estimated from previous instances are fused with new outputs from individual classifiers immediate after the outputs become available to establish updated state and class probability distributions in a Bayesian framework. Results are presented that demonstrate joint segmentation and tracking, target classification using imaging data, and fusion of imaging data with noisy and asynchronous auxiliary EW information under realistic simulation scenarios.

关键词： classification data fusion multitarget tracking Bayesian framework image processing data synchronization electronic warfare image segmentation

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A Generalized Information Matrix fusion Based Heterogeneous Track-to-Track fusion Algorithm

A Generalized Information Matrix Fusion Based Heterogeneous ...

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

作者： Tian, Xin Bar-Shalom, Yaakov Yuan, Ting Blasch, Erik Pham, Khanh Chen, Genshe Univ Connecticut Dept Elect & Comp Engn Storrs CT 06269 USA US Air Force Res Lab Sensors Directorate Wright Patterson AFB OH 45433 USA US Air Force Space Vehicle Directorate Kirtland AFB NM 87117 USA DMC Res Resource LLC Germantown MD USA

ISBN: (纸本)9780819486240

The problem of Track-to-Track fusion (T2TF) is very important for distributed tracking systems. It allows the use of the hierarchical fusion structure, where local tracks are sent to the fusion center (FC) as summaries of local information about the states of the targets, and fused to get the global track estimates. Compared to the centralized measurement-to-track fusion (CTF), the T2TF approach has low communication cost and is more suitable for practical implementation. Although having been widely investigated in the literature, most T2TF algorithms dealt with the fusion of homogenous tracks that have the same state of the target. However, in general, local trackers may use different motion models for the same target, and have different state spaces. This raises the problem of Heterogeneous Track-to-Track fusion (HT2TF). In this paper, we propose the algorithm for HT2TF based on the generalized Information Matrix fusion (GIMF) to handle the fusion of heterogenous tracks in the presence of possible communication delays. Compared to the fusion based on the LMMSE criterion, the proposed algorithm does not require the crosscovariance between the tracks for the fusion, which greatly simplify its implementation. Simulation results show that the proposed HT2TF algorithm has good consistency and fusion accuracy.

关键词： Tracking Heterogenous Track-to-Track fusion

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