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Conference on Multisensor, Multisource Information fusion - architectures, algorithms and applications 2010

作者： Wenzl, Konrad Ruser, Heinrich Kargel, Christian Bundeswehr Univ Munich Inst Measurement & Automat Div Sensor Technol & Measurement Syst D-85577 Neubiberg Munich Germany

ISBN: (纸本)9780819481740

A sensor network based on the LIDAR (LIght Detection And Ranging) principle is investigated in order to track persons inside a surveillance area and be able to identify security-relevant behavior. In order to minimize the overall sensor network complexity, power consumption and costs, we recently investigated the network topology based on a quality measure in terms of the number of nodes, measurement distance, width of the LIDAR beams and localization as well as classification performance. As a result, stationary beams with rather small opening angles of up to a few degrees are a good compromise. Since certain regions of the surveillance area are not directly assessed by the LIDAR beams, the accurate tracking of a target throughout the entire area of surveillance is challenging. We demonstrated that tracking based on a Kalman filter approach can nevertheless deliver satisfactory results for a single person inside the surveillance area. In this paper we focus on the task of reliably tracking two persons inside the surveillance area at the same time. The tracking of multiple moving targets is carried out by applying a Multiple Hypothesis Tracking filter approach. The localization and tracking performances are derived from simulations and experiments carried out with commercial laser scanners. In the near future, the rather expensive laser scanners will be replaced by appropriate LIDAR range finders.

关键词： sensor Network Surveillance Multiple Target Tracking Multiple Hypothesis Tracking Kalman Tracking

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Long-duration Fused Feature Learning Aided Tracking

Long-duration Fused Feature Learning Aided Tracking

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Conference on Multisensor, Multisource Information fusion - architectures, algorithms and applications 2010

作者： Ivey, Richard Horn, Joel Merkert, Raimund Boston Univ Dept Cognit & Neural Syst 677 Beacon St Boston MA 02215 USA

ISBN: (纸本)9780819481740

Multiple-hypothesis tracking (MHT) algorithms solve the report-to-track association problem(1-5) by accumulating kinematic evidence provided by one or more sensors over time to find likely correlations in the data. MHT technologies have long been applied to such problems as machine vision;automatic target tracking using radar moving target indicator (MTI) for ground, air, sea, and space vehicles;and video-based object tracking. However, relying on kinematic information alone to maintain reliable track in a multi-target scenario is problematic due to a plethora of issues such as sensor limitations, obscuration of the targets, or target/confuser proximity.(6-7) We present our track fusion algorithm, the Long-term Hypothesis Tree (LTHT) that solves the tracklet-to-tracklet association problem by using signature information to repair errors in kinematic tracks. LTHT provides a framework for using arbitrary target signatures such as spectral or shape characteristics to correct errors made by an MHT tracker. The LTHT represents high-level interactions among complex tracks by condensing kinematic track trajectories into a compact representation that can be efficiently maintained over much longer temporal scales than typical MHT trees.

关键词： Information fusion multiple-hypothesis tracking radar tracking machine vision

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Using Conceptual Spaces to Fuse Knowledge from Heterogeneous Robot Platforms

Using Conceptual Spaces to Fuse Knowledge from Heterogeneous...

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Conference on Multisensor, Multisource Information fusion - architectures, algorithms and applications 2010

作者： Kira, Zsolt Georgia Tech Mobile Robot Lab Atlanta GA 30308 USA

ISBN: (纸本)9780819481740

As robots become more common, it becomes increasingly useful for many applications to use them in teams that sense the world in a distributed manner. In such situations, the robots or a central control center must communicate and fuse information received from multiple sources. A key challenge for this problem is perceptual heterogeneity, where the sensors, perceptual representations, and training instances used by the robots differ dramatically. In this paper, we use Gardenfors' conceptual spaces, a geometric representation with strong roots in cognitive science and psychology, in order to represent the appearance of objects and show how the problem of heterogeneity can be intuitively explored by looking at the situation where multiple robots differ in their conceptual spaces at different levels. To bridge low-level sensory differences, we abstract raw sensory data into properties (such as color or texture categories), represented as Gaussian Mixture Models, and demonstrate that this facilitates both individual learning and the fusion of concepts between robots. Concepts (e.g. objects) are represented as a fuzzy mixture of these properties. We then treat the problem where the conceptual spaces of two robots differ and they only share a subset of these properties. In this case, we use joint interaction and statistical metrics to determine which properties are shared. Finally, we show how conceptual spaces can handle the combination of such missing properties when fusing concepts received from different robots. We demonstrate the fusion of information in real-robot experiments with a Mobile Robots Amigobot and Pioneer 2DX with significantly different cameras and (on one robot) a SICK lidar.

关键词： Conceptual Spaces Cognitive sensor fusion Heterogeneous Robot Teams

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Joint Manifolds for Data fusion

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2010年第10期19卷 2580-2594页

作者： Davenport, Mark A. Hegde, Chinmay Duarte, Marco F. Baraniuk, Richard G. Stanford Univ Dept Stat Stanford CA 94305 USA Rice Univ Dept Elect & Comp Engn Houston TX 77005 USA Princeton Univ Program Appl & Computat Math Princeton NJ 08544 USA

The emergence of low-cost sensing architectures for diverse modalities has made it possible to deploy sensor networks that capture a single event from a large number of vantage points and using multiple modalities. In many scenarios, these networks acquire large amounts of very high-dimensional data. For example, even a relatively small network of cameras can generate massive amounts of high-dimensional image and video data. One way to cope with this data deluge is to exploit low-dimensional data models. Manifold models provide a particularly powerful theoretical and algorithmic framework for capturing the structure of data governed by a small number of parameters, as is often the case in a sensor network. However, these models do not typically take into account dependencies among multiple sensors. We thus propose a new joint manifold framework for data ensembles that exploits such dependencies. We show that joint manifold structure can lead to improved performance for a variety of signal processing algorithms for applications including classification and manifold learning. Additionally, recent results concerning random projections of manifolds enable us to formulate a scalable and universal dimensionality reduction scheme that efficiently fuses the data from all sensors.

关键词： Camera networks classification data fusion manifold learning random projections sensor networks

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Multisensor, Multisource Information fusion: architectures, algorithms, and applications 2010

Multisensor, Multisource Information Fusion: Architectures, ...

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Multisensor, Multisource Information fusion: architectures, algorithms, and applications 2010

ISBN: (纸本)9780819481740

The proceedings contain 23 papers. The topics discussed include: capturing dynamics on multiple time scales: a multilevel fusion approach for cluttered electromagnetic data;classification of terrain using multi-resolution satellite/aerial imagery and lidar data;data fusion and classification using a hybrid intrinsic cellular inference network;fusion of ESM reports through Dempster-Shafer and Dezert-Smarandache theories;level 0-2 fusion model for ATR using fuzzy logic;multi-layered context impact modulation for enhanced focus of attention of situational awareness in persistent surveillance systems;global evaluation of focused Bayesian fusion;multiple hypothesis tracking of two persons using a network of lidar sensors with stationary and directional beams;a copula-based semi-parametric approach for footstep detection using seismic sensor networks;and integrating perception and problem solving to predict complex object behaviors.

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Cellular multi-core fusion-tracking system for multi-modal space-time signature analysis

Cellular multi-core fusion-tracking system for multi-modal s...

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IEEE International Workshop on Cellular Nanoscale Networks and their applications, (CNNA)

作者： Csaba Rekeczky Tibor Kozek Eutecus Inc. Berkeley CA USA Imagize LLC Berkeley CA USA

Summary form only given. A novel real-time signal processing device will be demonstrated designed and implemented for improved target feature extraction, discrimination, and tracking. The device utilizes a unique combination of advanced signal processing techniques for multi-spectral fusion and image analysis. It incorporates state-of-the-art algorithms and the associated electronics to combine the functions of a multi-spectral fusion (MSF) engine and a multi-target tracking and discrimination (MTTD) engine. The resulting compact MSF-MTTD system, currently is capable of processing image flows from two external sensors (e.g. infrared and visible) by utilizing the processing power of massively parallel cellular nonlinear processor architectures at different levels of processing. The compact (<2in ¿ 3) light-weight (<25 g), low-power (<5 W for the entire system) prototype of the multi-core MSF-MTTD engine and system has been implemented using low-power FPGAs and will be used and demonstrated in complex event detection scenarios.

关键词： Signal processing algorithms Engines Target tracking Infrared image sensors Signal design Feature extraction Image analysis sensor systems Prototypes Field programmable gate arrays

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Cellular multi-core fusion-tracking system

Cellular multi-core fusion-tracking system

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IEEE International Workshop on Cellular Nanoscale Networks and their applications, (CNNA)

作者： Csaba Rekeczky Tibor Kozek Eutecus Inc. Berkeley CA USA Imagize LLC Berkeley CA USA

ISBN: (纸本)9781424466795;9781424466788

A novel real-time signal processing device has been designed and implemented for improved target feature extraction, discrimination, and tracking. The device utilizes a unique combination of advanced signal processing techniques for multi-spectral fusion and image analysis. It incorporates state-of-the-art algorithm and the associated electronics to combine the functions of a multi-spectral fusion (MSF) engine and a multi-target tracking and discrimination (MTTD) engine. The resulting compact MSF-MTTD system, currently is capable of processing image flows from two external sensors (e.g. infrared and visible) by utilizing the processing power of massively parallel cellular nonlinear processor architectures at different levels of processing. Within this framework topographic data fusion (Stage 1) is followed by parallel feature extraction (Stage 2) and the analysis, tracking and discrimination (Stage 3) of multiple targets at ultra-high frame rates (>1000 fps). The compact (<2in ¿ 3) light-weight (<25 g), low-power (<5 W for the entire system) prototype of the multi-core MSF-MTTD engine and system has been implemented on high-end FPGAs and will be described in this paper.

关键词： Signal processing algorithms Target tracking Engines Feature extraction Infrared image sensors Signal design Image analysis sensor systems Prototypes Field programmable gate arrays

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Consensus algorithms for power-constrained wireless sensor networks

Consensus algorithms for power-constrained wireless sensor n...

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作者： Savas, Onur Boston University

学位级别：Ph.D.

Wireless sensor networks (SNets) is a cost-efficient technology that is typically comprised of many low-power, low-cost sensors. Current and potential applications of SNets include tracking, automation, control, surveillance, reconnaissance, security, and monitoring. All of these applications require some form of intelligent signal processing and decision making algorithms. Proposed algorithms involving fusion-center-based architectures do not scale well with increasing number of sensors. Instead, scalable, robust and power-efficient distributed signal processing and decision making algorithms are required in order to realize the full potential of SNets. Therefore, in this thesis, we propose and analyze power-efficient in-network signal processing and decision making algorithms for applications that are modeled as distributed classification, data aggregation and symmetric function computation problems. The work in this thesis is a step towards solving distributed signal processing and decision making problems in low-power distributed sensor networks in a cost-efficient manner. The low-power, constrained communication architecture of emerging sensor network technologies demand scalable, asynchronous, energy-efficient and robust sensor network architectures and algorithms. We address these concerns in this thesis by declaring the proposed algorithm as: (i) Scalable because communication is restricted to immediate one-hop neighbors. (ii) Asynchronous because no synchronization between sensor nodes or order of transmission of messages are required. (iii) Energy-efficient in terms of reduced total power and average power consumed. (iv) Robust to node and edge failures. In this thesis we propose our solution in two main categories. (i) We propose and analyze pair-wise message passing algorithms, where sensors achieve the optimal classification performance. (ii) We propose and analyze algorithms in order to calculate the measurement statistics of certain functions. Under b

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Soft adaptive fusion of sensor energy for large-scale sensor networks (SAFE)

Soft adaptive fusion of sensor energy for large-scale sensor...

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Multisensor, Multisource Information fusion: architectures, algorithms, and applications 2009

作者： Rababaah, Haroun Shirkhodaie, Amir Center of Excellence for Battlefield Sensor Fusion Department of Mechanical and Manufacturing Engineering Tennessee State University 3500 John Merritt Blvd. Nashville TN 37209 United States

ISBN: (纸本)9780819476111

Target tracking for network surveillance systems has gained significant interest especially in sensitive areas such as homeland security, battlefield intelligence, and facility surveillance. Most of the current sensor network protocols do not address the need for multi-sensor fusion-based target tracking schemes, which is crucial for the longevity of the sensor network. In this paper, we present an efficient fusion model for target tracking in a cluster-based large sensor networks. This new scheme is inspired by the image processing techniques by perceiving a sensor network as an energy map of sensor stimuli and applying typical image processing techniques on this map such as: filtering, convolution, clustering, segmentation, etc to achieve high-level perceptions and understanding of the situation. The new fusion model is called Soft Adaptive fusion of sensor Energies (SAFE). SAFE performs soft fusion of the energies collected by a local region of sensors in a large-scale sensor network. This local fusion is then transmitted by the head node to a base-station to update the common operation picture with evolving events of interest. Simulated scenarios showed that SAFE is promising by demonstrating a significant improvement in target tracking reliability, uncertainty, and efficiency. © 2009 SPIE.

关键词： sensor networks

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Energy logic (EL): A Novel fusion engine of multi-modality multi-agent data/information fusion for intelligent surveillance systems

Energy logic (EL): A Novel fusion engine of multi-modality m...

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Multisensor, Multisource Information fusion: architectures, algorithms, and applications 2009

作者： Rababaah, Haroun Shirkhodaie, Amir Center of Excellence for Battlefield Sensor Fusion Dept. of Mechanical and Manufacturing Engineering Tennessee State University 3500 John Merritt Blvd. Nashville TN 37209 United States

ISBN: (纸本)9780819476111

The rapidly advancing hardware technology, smart sensors and sensor networks are advancing environment sensing. One major potential of this technology is Large-Scale Surveillance Systems (LS3) especially for, homeland security, battlefield intelligence, facility guarding and other civilian applications. The efficient and effective deployment of LS3 requires addressing number of aspects impacting the scalability of such systems. The scalability factors are related to: computation and memory utilization efficiency, communication bandwidth utilization, network topology (e.g., centralized, ad-hoc, hierarchical or hybrid), network communication protocol and data routing schemes;and local and global data/information fusion scheme for situational awareness. Although, many models have been proposed to address one aspect or another of these issues but, few have addressed the need for a multi-modality multi-agent data/information fusion that has characteristics satisfying the requirements of current and future intelligent sensors and sensor networks. In this paper, we have presented a novel scalable fusion engine for multi-modality multi-agent information fusion for LS3. The new fusion engine is based on a concept we call: Energy Logic. Experimental results of this work as compared to a Fuzzy logic model strongly supported the validity of the new model and inspired future directions for different levels of fusion and different applications. © 2009 SPIE.

关键词： Scalability

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