Existing and Potential Standoff Explosives Detection Techniques (2004) / Chapter Skim
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3 Systems of Detection
3 Systems of Detection
Pages 35-55
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From page 35... ...
Standoff explosives detection must take into account more than the single sensor indication, because a system that relies on only a single source of information is too likely to make decisions with excessive false positives (false alarms)
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From page 36... ...
Also note that flooding the background with contamination or with mimics of the signal will effectively negate the operation of specific sensors using that detection means. Orthogonal Improvement over Any Individual Detection Technology A system that incorporates different, largely independent, modes for standoff explosive detection, while challenging, is better than a single sen
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From page 37... ...
37 of decision. is processing threat Y es present considered a Identification explosive is - An discrimination the threat the from - report environment positively an to Processing in - Discrimination step done - subsequent detection of The threat.
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From page 38... ...
However, an array of identical sensors does not necessarily increase the specificity of the system over that of one sensor, especially if a false alarm from a single sensor in the array results in a system-wide false alarm. A solution to increase the explosive detection system specificity would be to utilize multiple arrays of different sensor types and/or a single array of different sensor types along with an appropriate detector-decision scheme.
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From page 39... ...
The result coming from a standoff explosives detection system is not static, nor is it desirable that it be static. Indeed, the likelihood of accurate threat assessment arising from the orthogonal multisensor-type system should vary with time, (i.e., as more multidimensional information is gathered, the detection estimate accuracy could increase or decrease)
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From page 40... ...
Each path is made Example threat parameters, T i · Means of delivering the device to the point of detonation · Location and timing of detonation · Composition of the explosive detection Sensor Type C · Mass of the explosive of · Other components of the explosive device · Dispersive materials range detector or Sensor Type B notional) TUNKNOWN sensor j T( Sensor Type A another T3 using T1 T2 parameter's T4 threat A A threat parameter's ( Ti )
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From page 41... ...
These characteristics of the threats -- threat parameters -- can be used to identify the performance challenges and necessary capabilities when developing standoff explosives detection. The threat parameters include, at a minimum, the following.
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From page 42... ...
A rule of thumb is that the mass of explosive material required to cause equivalent damage increases as the cube of the distance (e.g., if the detonation-to-target distance is doubled, the required explosive mass increases by a factor of eight) .1 Thus a good way to protect a specific potential target is to limit how closely a bomb (especially a large bomb such as a vehicle bomb)
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From page 43... ...
Ambient temperature affects the vapor pressure of the explosive and can thus influence detection, and some detectors may cease to function at extreme temperatures. Atmospheric pressure and humidity can influence some detection technologies such as ion mobility spectrometry (IMS)
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From page 44... ...
Anomaly Detection and Response For standoff explosives detection in the scenarios under consideration here, there is a complication: explosives are not present very often, and false alarms are bad because they induce personnel to either disregard the alarm or disable it. Within the environment context of the operational system, conditions are either normal or abnormal.
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From page 45... ...
Design and Operational Requirements of a Threat Identification System To meet the challenges of threat characteristics, dynamic ambient conditions, human in-the-loop variability, and information obscuration, operational requirements can be developed to achieve a threat identification system with maximum effectiveness: 1. Quick identification.
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From page 46... ...
This could enable the threat monitoring personnel to interpret and evaluate the system's decisions and take appropriate actions by utilizing their experience as well. One would like the threat identification system to not only justify why certain hypotheses were proposed but also explain why certain other hypotheses were not proposed.
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From page 47... ...
and for systems of detection systems allowing for noisy input from many sensors. Of particular importance is the definition and evaluation of a full spectrum of "false positive" signals rang ing from detector reliability, legitimate signals that do not repre sent true threats, or operator interpretation of detector signals.
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From page 48... ...
On land, these sensor arrays may be deployed in all types of terrain for such applications as perimeter security and ground surveillance to name a few. Marine applications include harbor monitoring and fiber optic acoustic arrays for critical passive sonar platforms involved with anti submarine warfare."2 Distributed arrays can be fixed in one location with multiple sensors or distributed over a geographical area.
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From page 49... ...
and perhaps mobile sensors, distributed arrays of sensors, and the use of con vective streams with or without airborne adsorbing particles to gather chemical samples. Decision Fusion Figure 3.5 shows a sensor data fusion approach in which unprocessed sensor data are provided en masse to a data fusion center.
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From page 50... ...
50 the on is threat decision Yes a decsion present to Final explosive lead An making and data decision sensor unite center to based fusion Bayesian Nehman-Pearson Data performed fusion is Data fusion Data Sensor Sensor Sensor making. of decision explosives Indicators the of fusion-based Sensing environment explosive.
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From page 51... ...
Existing technologies all have limitations that can significantly impact either their sensitivity, their specificity, or both. However, by combining diverse technologies in a system, it is possible to provide an aggregate detection result that is adequate for standoff applications.
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From page 52... ...
Given the proposed orthogonal nature of the system, the addition of new sensors that exploit novel orthogonal means of detection may be a highly appropriate response to compromised system capabilities. Adaptation requires mechanisms for learning, and especially for learning from system detection failures expressed either as false alarms or as analogues of false negatives.
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From page 53... ...
System Effectiveness FIGURE 3.7 System concept showing segregation of detection -- done in an envi ronment -- from the discrimination processing of the sensor signals and the iden tification of a threat, and from subsequent decision to report the threat.
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From page 54... ...
Thus, there should be a "model refinement" or "learning" capability in the system that is active during the detection experience, thereby mitigating the difficulty of tuning the system on simulated or artificial threats and yet requiring the system to identify very low rate real threats. It is generally assumed that the detection methods involve several different technologies, operating in semi- or completely orthogonal modes.
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From page 55... ...
SYSTEMS OF DETECTION 55 sor-detector systems. Elements of the system execute tasks including detection and discrimination, all supporting the central authority of identification and final decision.
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