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CB detection and early warning - Fusing disparate sensors into the detection process - Program overview

CB detection and early warning - Fusing disparate sensors in...

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conference on Multisensor, Multisource Information fusion - architectures, algorithms and applications 2003

作者： Birenzvige, A Sickenberger, D Reyes, F Underwood, W Reif, C Gonski, D Fargues, M Nelson, B USA SBCCOM Edgewood Chem & Biol Ctr Aberdeen Proving Ground MD 21010 USA

ISBN: (纸本)0819449598

Improved situational awareness is a primary goal for the Objective Force. Knowing where the enemy is and what are the threats to his troops provides the commander with the information he needs to plan his mission and provide his forces with maximum protection from the variety of threats that are present on the battlefield. sensors play an important role in providing critical information to enhance situational awareness. The sensors that are used on the battlefield include, among others, seismic, acoustic, and cameras in different spectral ranges of the electromagnetic spectrum. These sensors help track enemy movement and serve as part of an intrusion detection system. Characteristically these sensors are relatively cheap and easy to deploy. Chemical and biological agent detection is currently relegated to sensors that are specifically designed to detect these agents. Many of these sensors are collocated with the troops. By the time alarm is sounded the troops have already been exposed to the agent. In addition, battlefield contaminants frequently interfere with the performance of these sensors and result in false alarms. Since operating in a contaminated environment requires the troops to don protective garments that interfere with their performance we need to reduce false alarms to an absolute minimum. The Edgewood Chemical and Biological Center (ECBC) is currently conducting a. study to examine the possibility of detecting chemical and biological weapons as soon as they are deployed. For that purpose we conducted a field test in which the acoustic, seismic and electro-magnetic signatures of conventional and simulated chemical / biological 155mm artillery shells were recorded by an array of corresponding sensors. Initial examination of the data shows a distinct differences in the signatures of these weapons. In this paper we will provide detailed descriptions of the test procedures. We will describe the various sensors used and describe the differences in the signa

关键词： battlefield sensors weapons discrimination sensor and data fusion

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Pedestrian detection by Multi-spectral fusion

Pedestrian detection by Multi-spectral fusion

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conference on Multisensor, Multisource Information fusion

作者： Ma, Yunqian Wang, Zheng Bazakos, Mike Honeywell Labs 3660 Technol Dr Minneapolis MN 55418 USA Honeywell Technol Solut Lab Shanghai 201203 Peoples R China

ISBN: (纸本)0819462985

Security systems increasingly rely on the use of Automated Video Surveillance (AVS) technology. In particular the use of digital video renders itself to internet and local communications, remote monitoring, and to computer processing. AVS systems can perform many tedious and repetitive tasks currently performed by trained security personnel. AVS technology has already made some significant steps towards automating some basic security functions such as: motion detection, object tracking and event-based video recording. However, there are still many problems associated with just these automated functions, which need to be addressed further. Some examples of these problems are: the high "false alarm rate" and the "loss of track" under total or partial occlusion, when used under a wide range of operational parameters (day, night, sunshine, cloudy, foggy, range, viewing angle, clutter, etc.). Current surveillance systems work well only under a narrow range of operational parameters. Therefore, they need be hardened against a wide range of operational conditions. In this paper, we present a Multi-spectral fusion approach to perform accurate pedestrian segmentation under varying operational parameters. Our fusion method combines the "best" detection results from the visible images and the "best" from the thermal images. Commonly, the motion detection results in the visible images are easily affected by noise and shadows. The objects in the thermal image are relatively stable, but they may be missing some parts of the objects, because they thermally blend with the background. Our method makes use of the "best" object components and de-emphasize the "not best".

关键词： pedestrian detection sensor fusion surveillance level set

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Flight test capabilities for real-time multiple target detection and tracking for airborne surveillance and maritime domain awareness

Flight test capabilities for real-time multiple target detec...

引用

conference on Optics and Photonics in Global Homeland Security iv

作者： Gorin, Brian A. Waxman, Allen BAE Syst Sensor Syst I&S 450 Pulaski Rd Greenlawn NY 11740 USA BAE Syst Network Syst AIT Burlington MA 01803 USA

ISBN: (纸本)9780819471369

Recent flight tests of the Airborne Reconfigurable Imaging System (ARIS) developed by BAE Systems have demonstrated several key capabilities for day-night multiple target detection and tracking of maritime surface vessels. The flight test system includes the integration of BAE Systems-developed real-time image processing algorithms with multispectral band electro-optical (EO) (RGB)/mid-wave infrared (MWIR) sensors and a COTS turreted system. Presented here are significant flight test results that demonstrate real-time multispectral sensor image co-registration, geo-registration, fusion, target detection and tracking. High performance geo-pointing and line-of-sight stabilization capabilities enable the airborne system to provide maritime domain awareness objectives for search and rescue, persistent surveillance of moving and stationary targets, contraband traffic control through detection and tracking of concealed vessels, and autonomous tracking of both moving and stationary vessels. Solar glint and cloud coverage false alarms are minimized while multiple target detections and tracks are maintained. The fundamental real-time processing methodologies used for ARIS are applied to a COTS multiple field of view system with high-resolution RGB and mid-wave infrared (MWIR) video rate imaging. The techniques discussed for this four spectral band system can be applied to both extended multispectral systems (greater than four spectral bands) and hyperspectral systems to further enhance system capabilities for analogous terrestrial applications.

关键词： multispectral multi-target detection and tracking turreted airborne geo-pointing maritime domain awareness persistent surveillance real time fusion

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Mitigation of Motion Artifacts in Pulse Oximetry through Redundant sensors 25

Mitigation of Motion Artifacts in Pulse Oximetry through Red...

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25th IEEE conference on Signal Processing - algorithms, architectures, Arrangements, and applications (IEEE SPA)

作者： Alkhoury, Ludvik Choi, Jiwon Wang, Chizhong Mahoney, Sean Shender, Barry S. Hrebien, Leonid Kam, Moshe New Jersey Inst Technol Dept Elect & Comp Engn Newark Coll Engn Newark NJ 07102 USA Athena GTX Regulatory Affairs Dept Athena IA 50113 USA Naval Air Warfare Ctr Aircraft Div Human Syst Dept Patuxent River MD 20670 USA Drexel Univ Dept Elect & Comp Engn Philadelphia PA 19104 USA

ISBN: (数字)9788362065424

ISBN: (纸本)9788362065424

Photoplethysmography (PPG) signals are used in pulse oximetry to calculate peripheral capillary oxygen saturation (SpO(2)) levels. It is well known that the accuracy of the calculated SpO(2) is susceptible to motion noise. Using field data collected from human subjects during rest and while exhibiting medium to high-intensity physical exercises, we show how sensor redundancy can mitigate motion effects by employing two channels of red and infrared PPG signals. We demonstrate the advantage of redundancy using two SpO(2) calculation methods. These methods are widely used "Red over Infrared" (RoI) method and the Discrete Saturation Transform (DST) algorithm both, assisted by pre-filtering by a heart-rate tuned comb filter. The data were collected from individuals at rest and while exercising. SpO(2) levels are calculated for two channels, each using a red and an infrared PPG signal. We integrated the SpO(2) computed from the two channels with a Kalman filter (KF) and computed the SpO(2) mean absolute error (MAE) from each channel individually and after integrating the two channels. The SpO(2) MAEs of each one of the two channels separately were greater (and statistically significant) than the SpO(2) MAE obtained from the two channels integrated with a KF. We observed that when two channels of PPG (red and infrared) signals were available, then combining SpO(2) levels obtained from these two channels exhibited a smoother and more reliable estimate of the blood oxygen saturation level as compared the SpO(2) levels computed from each channel separately. The reason is that if the data for the two channels are collected at sufficient physical distance from one another, the motion artifacts affecting the two channels are uncorrelated. The KF is then able to use past measurements and modeling of the dynamics to attenuate the effect of the motion artifacts.

关键词： pulse oximetry peripheral capillary oxygen saturation (SpO(2)) electrocardiography (ECG) motion artifact motion resistant oximetry data fusion comb filter red over infrared Discrete Saturation Transform (DST) Kalman Filter wearable unit multiple photoplethysmography (PPG) channels

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Prior knowledge integration of road dependant ground target behaviour for improving tracking reliability

Prior knowledge integration of road dependant ground target ...

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conference on Multisensor, Multisource Information fusion

作者： Gattein, S Vannoorenberghe, P Contat, M EADS S&DE Voie Future F-27106 Val De Reuil France

ISBN: (纸本)0819457981

This paper describes an algorithm for tracking ground targets, mainly based on measurements from a MTI radar. We examine the use of prior knowledge on target type for automatically improving the performance of a purely kinematic tracker. This tracker is an adaptation from a Variable Structure Interacting Multiple Model (VS-IMM) estimator acting within an S-Dimensional Assignment method. The algorithm can thus track several targets with false alarms, and the variable structure allows to use only the relevant dynamic models relative to roads in the vicinity of the target's prediction. However the sets of possible on-road and off-road models do not interact as in the usual IMM mechanism. Instead the on-road behaviour of a target across S-I radar scans is optimally managed in a hypothesis tree, since we do not mix incompatible road models. However, both global on-road and off-road behaviours are handled by mode transition probabilities, making it possible to track targets that change their behaviour regarding the road network with the same algorithm. Next an automatic measured type data integration scheme is examined, that can be connected to the above kinematic framework. The incoming measured type data is modelled by a belief function to reflect uncertainty, as well as the sensor's reliability. Simulation results show the operation of the kinematic tracker for road targets, illustrating the relevance of the variable structure dynamic model set.

关键词： data fusion target tracking VS-MM S-D assignment belief function

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A novel approach to guarantee causal message ordering in pre-planned wireless sensor networks

A novel approach to guarantee causal message ordering in pre...

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12th International conference on algorithms and architectures for Parallel Processing, ICA3PP 2012

作者： Kim, Chayoung Ahn, Jinho Dept. of Computer Science College of Natural Science Kyonggi University Suwon Gyeonggi-do 443-760 Korea Republic of

ISBN: (纸本)9783642330643

Data fusion often depends on the time of occurrence of fused sensor readings, called as temporal ordering, which is defined with respect to a single message, or as causal ordering to guarantee dependency relationships between messages in many-to-many communication patterns. There are some temporal ordering protocols based on physical time synchronization in publish/subscribe (P/S) paradigm of wireless sensor networks, but there exist little research works on development of causal ordering protocols based on logical time in P/S of wireless sensor networks (WSNs). Causal message ordering is more useful for most distributed applications in which a large number of sensor nodes request cooperating to fuse their data in WSNs. Temporal ordering is not sufficient for these distributed applications because it is not defined for dependency relationships between these messages. Also, many-to-many communication patterns attempt to address the problem of providing scalability of data propagation, guaranteeing message delivery order and supporting overlapping multicast groups in WSNs. In this paper, we present a novel approach based on gossiping and firefly synchronization instead of physical time synchronization, guaranteeing the causal message ordering property in P/S of WSNs. In the proposed protocol, every sensor broker disseminates the multicast message including the latest time-stamped information that represents the gossip round in which the message is generated to subscribers for causal message ordering. The latest time-stamped information is represented using colors. Its scalability feature might be highly suitable for the area of the applications requiring only the minimum causal information of message delivery with flexible consistency by cooperating to fuse their data. © 2012 Springer-Verlag.

关键词： Scalability

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Spatiotemporal Modeling Encounters 3D Medical Image Analysis: Slice-Shift UNet with Multi-View fusion 7

Spatiotemporal Modeling Encounters 3D Medical Image Analysis...

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7th International conference on Machine Vision and applications (ICMVA)

作者： Ugwu, Cynthia I. Casarin, Sofia Lanz, Oswald Free Univ Bozen Bolzano Bolzano Italy

ISBN: (纸本)9798400716553

As a fundamental part of computational healthcare, Computer Tomography (CT) and Magnetic Resonance Imaging (MRI) provide volumetric data, making the development of algorithms for 3D image analysis a necessity. Despite being computationally cheap, 2D Convolutional Neural Networks can only extract spatial information. In contrast, 3D CNNs can extract three-dimensional features, but they have higher computational costs and latency, which is a limitation for clinical practice that requires fast and efficient models. Inspired by the field of video action recognition we propose a new 2D-based model dubbed Slice SHift UNet (SSH-UNet) which encodes three-dimensional features at 2D CNN's complexity. More precisely multi-view features are collaboratively learned by performing 2D convolutions along the three orthogonal planes of a volume and imposing a weights-sharing mechanism. The third dimension, which is neglected by the 2D convolution, is reincorporated by shifting a portion of the feature maps along the slices' axis. The effectiveness of our approach is validated in Multi-Modality Abdominal Multi-Organ Segmentation (AMOS) and Multi-Atlas Labeling Beyond the Cranial Vault (BTCV) datasets, showing that SSH-UNet is more efficient while on par in performance with state-of-the-art architectures.

关键词： healthcare volumetric data neural networks segmentation

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Multisensor information compression and reconstruction

Multisensor information compression and reconstruction

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

作者： Bing, Du Liang, Liu Jun, Zhang School of Electronics and Information Engineering Beihang Univ. XueYuan Road 37 Haidian District Beijing 100191 China

ISBN: (纸本)9780819476111

In this paper, we propose a method of sampled data compression and reconstruction using the theory of distributed compressed sensing for wireless sensor network, in which the correlation between the sensors is considered for joint sparsity representation, compression and reconstruction of the signals. And incoherent random projection CS matrix in each sensor is as encoding matrix to generate compressed measurements for storing, delivering and processing. The reconstruction algorithm with both acceptable complexity and precision is developed for noise corrupted measurements by fully utilizing of correlations diversity. The simulation shows that the number of measurements only slightly larger than the sparsity of the sampled sensor data is needed for successful recovery. © 2009 SPIE.

关键词： Wireless sensor networks

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Multi-sensor, multi-source information fusion: architecture, algorithms, and applications - a panoramic overview

Multi-sensor, multi-source information fusion: architecture,...

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International conference on Computational Cybernetics (ICCC)

作者： B.V. Dasarathy Huntsville AL USA

This plenary presentation offers a panoramic overview of the field of multi-sensor, and/or multi-source information fusion from three complementary perspectives, namely, architectures, algorithms, and applications. This overview is developed through addressing the questions of 'what', 'why', 'when', and last but not least 'how' of information fusion and illustrating the answers with appropriate algorithmic and applicative examples

关键词： Feature extraction Iron Taxonomy Data processing Data mining Biosensors sensor fusion Security Bibliographies

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Performance evaluation on contour extraction using Hough transform and RANSAC for multi-sensor data fusion applications in industrial food inspection

Performance evaluation on contour extraction using Hough tra...

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IEEE Signal Processing algorithms, architectures, Arrangements, and applications conference

作者： M. Kroger W. Sauer-Greff R. Urbansky M. Lorang M. Siegrist Chair for Communications Engeneering University of Kaiserslautern WIPOTEC GmbH

ISBN: (纸本)9781509026616

For multi-sensor data fusion applications the accurate alignment of different sensor data is essential for the proper combination of matching features. In food inspection system the boxing often is in a rectangular shape. This knowledge can be used to rectify the image data, an important step in the alignment stage. In case of low contrast between boxing and background, the detected contour may differ significantly from the actual values. In this paper the performance of the Hough transform and the RANdom SAmple Consensus (RANSAC)-algorithm are evaluated relating to the correct extraction of the boxing contour out of contour data distorted by position errors of the outer shape. The evaluation results indicate the superiority of the RANSAC algorithm with respect to scalability, robustness and execution time.

关键词： Hough transform Food inspection Edge detection boxing multi-sensor data fusion sensor Data Performance Evaluation location error contour image data execution time

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