U.S. Army Soldier and Biological Chemical Command counterproliferation long-range biological standoff detection system (CP LR-BSDS)

1999 ◽  
Author(s):  
Lawrence A. Condatore, Jr. ◽  
Richard B. Guthrie ◽  
Bruce J. Bradshaw ◽  
Kenyon E. Logan ◽  
Larry S. Lingvay ◽  
...  
Keyword(s):  
Long Range ◽  
Detection System ◽  
Standoff Detection

Short-range biological standoff detection system (SR-BSDS)

2000 ◽  
Author(s):  
V. James Cannaliato ◽  
Bruce W. Jezek ◽  
Larry Hyttinen ◽  
John B. Strawbridge ◽  
William J. Ginley
Keyword(s):  
Short Range ◽  
Detection System ◽  
Standoff Detection

scholarly journals Multispectral LIF-Based Standoff Detection System for the Classification of CBE Hazards by Spectral and Temporal Features

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2524 ◽  
Author(s):  
Lea Fellner ◽  
Marian Kraus ◽  
Florian Gebert ◽  
Arne Walter ◽  
Frank Duschek
Keyword(s):  
Spectral Data ◽  
Classification Accuracy ◽  
Detection System ◽  
Laser Source ◽  
Nanosecond Laser ◽  
Time Resolved Fluorescence ◽  
Data Set ◽  
Time Resolved ◽  
Standoff Detection

Laser-induced fluorescence (LIF) is a well-established technique for monitoring chemical processes and for the standoff detection of biological substances because of its simple technical implementation and high sensitivity. Frequently, standoff LIF spectra from large molecules and bio-agents are only slightly structured and a gain of deeper information, such as classification, let alone identification, might become challenging. Improving the LIF technology by recording spectral and additionally time-resolved fluorescence emission, a significant gain of information can be achieved. This work presents results from a LIF based detection system and an analysis of the influence of time-resolved data on the classification accuracy. A multi-wavelength sub-nanosecond laser source is used to acquire spectral and time-resolved data from a standoff distance of 3.5 m. The data set contains data from seven different bacterial species and six types of oil. Classification is performed with a decision tree algorithm separately for spectral data, time-resolved data and the combination of both. The first findings show a valuable contribution of time-resolved fluorescence data to the classification of the investigated chemical and biological agents to their species level. Temporal and spectral data have been proven as partly complementary. The classification accuracy is increased from 86% for spectral data only to more than 92%.

Short-range biological standoff detection system (SR-BSDS)

1999 ◽  
Author(s):  
William Suliga ◽  
Ralph L. Burnham ◽  
Timothy Deely ◽  
William Gavert ◽  
Mark S. Pronko ◽  
...  
Keyword(s):  
Short Range ◽  
Detection System ◽  
Standoff Detection

SMART on-board multi-sensor obstacle detection system for improvement of rail transport safety

2021 ◽  
pp. 095440972110327
Author(s):  
Danijela Ristić-Durrant ◽  
Muhammad Abdul Haseeb ◽  
Milan Banić ◽  
Dušan Stamenković ◽  
Miloš Simonović ◽  
...  
Keyword(s):  
Long Range ◽  
Environmental Conditions ◽  
Detection System ◽  
Distance Estimation ◽  
Obstacle Detection ◽  
Rail Transport ◽  
Night Vision ◽  
Light Illumination ◽  
Project Smart ◽  
Night Illumination

This paper presents an on-board multi-sensor system which is able to detect obstacles and estimate their distances in railway scenes in different illumination conditions. The system was developed within the H2020 Shift2Rail project SMART (Smart Automation of Rail Transport) and aims at increasing the safety of rail transport by detecting obstacles on the rail tracks ahead of a moving train in order to reduce the number of collisions. The system hardware consists of cameras of different types integrated into a specially designed housing, mounted on the front of the train. Multiple vision sensors complement each other in order to handle different illumination and environmental conditions. The system software uses a novel machine learning-based method that is suited to a particular challenge of railway operations, the need for long-range obstacle detection and distance estimation. The development of this method used a long-range railway dataset, which was specifically generated for this project. Evaluation results of reliable obstacle detection in various environmental conditions using the SMART RGB camera in day light illumination conditions and using the SMART Night Vision sensor in poor (night) illumination conditions are presented. The results demonstrate both the potential of the on-board SMART obstacle detection system in the operational railway environment and the benefit of the use of different cameras to be more independent of light and environmental conditions.

FT-IR Standoff Detection of Thermally Excited Emissions of Trinitrotoluene (TNT) Deposited on Aluminum Substrates

2013 ◽  
Vol 67 (2) ◽  
pp. 181-186 ◽  
Author(s):  
John R. Castro-Suarez ◽  
Leonardo C. Pacheco-Londoño ◽  
Miguel Vélez-Reyes ◽  
Max Diem ◽  
Thomas J. Tague ◽  
...  
Keyword(s):  
Energetic Materials ◽  
Solid Phase ◽  
Detection System ◽  
Target Surface ◽  
Least Squares Regression ◽  
Passive Mode ◽  
Standoff Detection ◽  
Highly Energetic Materials ◽  
Aluminum Plates ◽  
Aluminum Substrates

A standoff detection system was assembled by coupling a reflecting telescope to a Fourier transform infrared spectrometer equipped with a cryo-cooled mercury cadmium telluride detector and used for detection of solid-phase samples deposited on substrates. Samples of highly energetic materials were deposited on aluminum substrates and detected at several collector-target distances by performing passive-mode, remote, infrared detection measurements on the heated analytes. Aluminum plates were used as support material, and 2,4,6-Trinitrotoluene (TNT) was used as the target. For standoff detection experiments, the samples were placed at different distances (4 to 55 m). Several target surface temperatures were investigated. Partial least squares regression analysis was applied to the analysis of the intensities of the spectra obtained. Overall, standoff detection in passive mode was useful for quantifying TNT deposited on the aluminum plates with high confidence up to target–collector distances of 55 m.

Design of a long-range IR detection system

1999 ◽  
Author(s):  
S. S. Negi ◽  
Om P. Nijhawan ◽  
A. K. Sahay ◽  
Harpal S. Singh
Keyword(s):  
Long Range ◽  
Detection System ◽  
Ir Detection

scholarly journals Autonomous long-range drone detection system for critical infrastructure safety

2021 ◽  
Author(s):  
Xindi Zhang ◽  
Kusrini Kusrini
Keyword(s):  
Long Range ◽  
Situation Awareness ◽  
Critical Infrastructure ◽  
Detection System ◽  
Focal Length ◽  
Low Cost ◽  
System Logs ◽  
Continuous Stream ◽  
Hardware Component ◽  
Fully Connected

AbstractThe development of unmanned aerial vehicles has been identified as a potential source of a weapon for causing operational disruptions against critical infrastructures. To mitigate and neutralise the threat posed by the misuse of drones against malicious and terrorist activity, this paper presents a holistic design of a long-range autonomous drone detection platform. The novelty of the proposed system lies in the confluence between the design of hardware and software components to effective and efficient localisation of the intruder objects. The research presented in the paper proposes the design and validation of a situation awareness component which is interfaced with the hardware component for controlling the focal length of the camera. The continuous stream of media data obtained from the region of vulnerability is processed using the object detection that is built on region based fully connected neural network. The novelty of the proposed system relies on the processing of multi-threaded dual-media input streams that are evaluated to mitigate the latency of the system. Upon the successful detection of malicious drones, the system logs the occurrence of intruders that consists of both event description and the associated media evidence for the deployment of the mitigation strategy. The analytics platform that controls the signalling of the low-cost sensing equipment contains the NVIDIA GeForce GTX 1080 for detecting drones. The experimental testbeds developed for the validation of the proposed system has been constructed to include environments and situations that are commonly faced by critical infrastructure operators such as the area of protection, drone flight path, tradeoff between the angle of coverage against the distance of coverage. The validation of the proposed system has resulted in yielding a range of intruder drone detection by 250m with an accuracy of 95.5%.

scholarly journals High-Precision Low-Cost Gimballing Platform for Long-Range Railway Obstacle Detection

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 474
Author(s):  
Elio Hajj Assaf ◽  
Cornelius von von Einem ◽  
Cesar Cadena ◽  
Roland Siegwart ◽  
Florian Tschopp
Keyword(s):  
Long Range ◽  
High Precision ◽  
High Speed ◽  
Detection System ◽  
Low Cost ◽  
Obstacle Detection ◽  
Numerical Control ◽  
Railway Network ◽  
Lever System ◽  
Novel Approach

Increasing demand for rail transportation results transportation by rail, resulting in denser and more high-speed usage of the existing railway network, making makes new and more advanced vehicle safety systems necessary. Furthermore, high traveling speeds and the greatlarge weights of trains lead to long braking distances—all of which necessitates Long braking distances, due to high travelling speeds and the massive weight of trains, necessitate a Long-Range Obstacle Detection (LROD) system, capable of detecting humans and other objects more than 1000 m in advance. According to current research, only a few sensor modalities are capable of reaching this far and recording sufficiently accurate enoughdata to distinguish individual objects. The limitation of these sensors, such as a 1D-Light Detection and Ranging (LiDAR), is however a very narrow Field of View (FoV), making it necessary to use ahigh-precision means of orienting to target them at possible areas of interest. To close this research gap, this paper presents a novel approach to detecting railway obstacles by developinga high-precision pointing mechanism, for the use in a future novel railway obstacle detection system In this work such a high-precision pointing mechanism is developed, capable of targeting aiming a 1D-LiDAR at humans or objects at the required distance. This approach addresses To address the challenges of a low target pricelimited budget, restricted access to high-precision machinery and equipment as well as unique requirements of our target application., a novel pointing mechanism has been designed and developed. By combining established elements from 3D printers and Computer Numerical Control (CNC) machines with a double-hinged lever system, simple and cheaplow-cost components are capable of precisely orienting an arbitrary sensor platform. The system’s actual pointing accuracy has been evaluated using a controlled, in-door, long-range experiment. The device was able to demonstrate a precision of 6.179 mdeg, which is at the limit of the measurable precision of the designed experiment.

Results of a UV TEPS-Raman energetic detection system (TREDS-2) for standoff detection

2009 ◽  
Author(s):  
Robert D. Waterbury ◽  
Alan R. Ford ◽  
Jeremy B. Rose ◽  
Edwin L. Dottery
Keyword(s):  
Detection System ◽  
Standoff Detection
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