Aviation security requirements adopted in 2014 require liquids to be screened at most airports throughout Europe, North America and Australia. Cobalt’s unique Spatially Offset Raman Spectroscopy (SORS™) technology has proven extremely effective at screening liquids, aerosols and gels (LAGS) with extremely low false alarm rates. SORS is compatible with a wide range of containers, including coloured, opaque or clear plastics, glass and paper, as well as duty-free bottles in STEBs (secure tamper-evident bags). Our award-winning Insight range has been specially developed for table-top screening at security checkpoints. Insight systems use our patented SORS technology for rapid and accurate chemical analysis of substances in unopened non-metallic containers. Insight100M™ and the latest member of the range - Insight200M™ - also screen metallic containers. Our unique systems screen liquids, aerosols and gels with the highest detection capability and lowest false alarm rates of any ECAC-approved scanner, with several hundred units already in use at airports including eight of the top ten European hubs. This paper presents an analysis of real performance data for these systems.
Raman spectroscopy allows the acquisition of molecularly specific signatures of pure compounds and mixtures making it a popular method for material identification applications. In hazardous materials, security and counter terrorism applications, conventional handheld Raman systems are typically limited to operation by line-of-sight or through relatively transparent plastic bags / clear glass vials. If materials are concealed behind thicker, coloured or opaque barriers it can be necessary to open and take a sample. Spatially Offset Raman Spectroscopy (SORS)[1] is a novel variant of Raman spectroscopy whereby multiple measurements at differing positions are used to separate the spectrum arising from the sub layers of a sample from the spectrum at the surface. For the first time, a handheld system based on SORS has been developed and applied to hazardous materials identification. The system - "Resolve" - enables new capabilities in the rapid identification of materials concealed by a wide variety of non-metallic sealed containers such as; coloured and opaque plastics, paper, card, sacks, fabric and glass. The range of potential target materials includes toxic industrial chemicals, explosives, narcotics, chemical warfare agents and biological materials. Resolve has the potential to improve the safety, efficiency and critical decision making in incident management, search operations, policing and ports and border operations. The operator is able to obtain a positive identification of a potentially hazardous material without opening or disturbing the container - to gain access to take a sample - thus improving safety. The technique is fast and simple thus suit and breathing gear time is used more efficiently. SORS also allows Raman to be deployed at an earlier stage in an event before more intrusive techniques are used. Evidential information is preserved and the chain of custody protected. Examples of detection capability for a number of materials and barrier types are presented below.
With the continuing threat to aviation security from homemade explosive devices, the restrictions on taking a volume of liquid greater than 100 ml onto an aircraft remain in place. From January 2014, these restrictions will gradually be reduced via a phased implementation of technological screening of Liquids, Aerosols and Gels (LAGs). Raman spectroscopy offers a highly sensitive, and specific, technique for the detection and identification of chemicals. Spatially Offset Raman Spectroscopy (SORS), in particular, offers significant advantages over conventional Raman spectroscopy for detecting and recognizing contents within optically challenging (Raman active) containers. Containers vary enormously in their composition; glass type, plastic type, thickness, reflectance, and pigmentation are all variable and cause an infinite range of absorbances, fluorescence backgrounds, Rayleigh backscattered laser light, and container Raman bands. In this paper we show that the data processing chain for Cobalt Light Systems’ INSIGHT100 bottlescanner is robust to such variability. We discuss issues of model selection for the detection stage and demonstrate an overall detection rate across a wide range of threats and containers of 97% with an associated false alarm rate of 0.1% or lower.
Recently, Spatially Offset Raman Spectroscopy (SORS) has been discussed as a novel method for the screening of
liquids, aerosols and gels (LAGs) at airports and for other security applications. SORS is an optical spectroscopic
method which enables the precise chemical identification of substances from a reference list and, due to the rich spectral
information, has an inherently high probability of detection and low false alarm rate. The method is generally capable of
screening substances inside non-metallic containers such as plastic and glass bottles. SORS is typically successful
through opaque plastic and coloured glass, which are often challenging for conventional backscatter Raman
spectroscopy. SORS is performed in just a few seconds by shining a laser light onto the container and then measuring the
Raman signal at the excitation point but also at one or more offset positions. Each measurement has different relative
orthogonal contributions from the container and contents Raman spectra, so that, with no prior knowledge, the pure
Raman spectra of both the container and contents can be
extracted - either by scaled subtraction or via multivariate
statistical methods in an automated process. In this paper, the latest results will be described from a prototype SORS
device designed for aviation security and the advantages and limitations of SORS will be discussed.
Spatially Offset Raman Spectroscopy (SORS) is a novel technique used to identify the chemical Raman signature of
threat materials within a few seconds through common non-metallic containers, including those containers which may
not yield to inspection by conventional backscatter Raman. In particular, some opaque plastic containers and coloured
glass bottles can be difficult to analyze using conventional backscatter Raman because the signal from the contents is
often overwhelmed by the much stronger Raman signal and/or fluorescence originating from the container itself. SORS
overcomes these difficulties and generates clean Raman spectra from both the container and the contents with no prior
knowledge of either. This is achieved by making two, or more, Raman measurements at various offsets between the
collection and illumination areas, each containing different proportions of the fingerprint signals from the container and
content materials. Using scaled subtraction, or multivariate statistical methods, the two orthogonal signals can be
separated numerically, thereby providing a clean Raman spectrum of the contents without contamination from the
container. Consequently, SORS promises to significantly improve threat detection capability and decrease the falsealarm
rate compared with conventional Raman spectroscopy making it considerably more suitable as an alarm resolution
methodology (e.g. at airports). In this paper, the technique and method are described and a study of offset value
optimization is described illustrating the difference between one and two fixed spatial offsets. It is concluded that two
fixed offsets yield an improvement in the SORS measurement which will help maximize the threat detection capability.
The specular nature of Radar imagery causes problems for ATR as small changes to the configuration of targets can result in significant changes to the resulting target signature. This adds to the challenge of constructing a classifier that is both robust to changes in target configuration and capable of generalizing to previously unseen targets. Here, we describe the application of a nonlinear Radial Basis Function (RBF) transformation to perform feature extraction on millimeter-wave (MMW) imagery of target vehicles. The features extracted were used as inputs to a nearest-neighbor classifier to obtain measures of classification performance. The training of the feature extraction stage was by way of a loss function that quantified the amount of data structure preserved in the transformation to feature space. In this paper we describe a supervised extension to the loss function and explore the value of using the supervised training process over the unsupervised approach and compare with results obtained using a supervised linear technique (Linear Discriminant Analysis --- LDA). The data used were Inverse Synthetic Aperture Radar (ISAR) images of armored vehicles gathered at 94GHz and were categorized as Armored Personnel Carrier, Main Battle Tank or Air Defense Unit. We find that the form of supervision used in this work is an advantage when the number of features used for classification is low, with the conclusion that the supervision allows information useful for discrimination between classes to be distilled into fewer features. When only one example of each class is used for training purposes, the LDA results are comparable to the RBF results. However, when an additional example is added per class, the RBF results are significantly better than those from LDA. Thus, the RBF technique seems better able to make use of the extra knowledge available to the system about variability between different examples of the same class.
This paper examines the use of a nonlinear dimensionality reduction scheme for feature extraction applied to ISAR images of armored targets. The features are then used in a nearest-neighbor classifier to evaluate their utility in achieving classification performance that is robust to changes in exterior detail of vehicles (for example open or closed hatches and storage boxes etc.). In addition to robustness a classifier is desired to generalize and correctly classify an example of a class that was not present in the training process (for example if the training process represents the Main Battle Tank class with a T72 and a Chieftain, a successful classification is desired when the system is presented with a Challenger). The proportion of the original data structure that has been retained in the dimension reducing transformation is calculated through the use of a loss function. The structure preserving properties of a nonlinear projection using Radial Basis Functions are compared with a linear projection obtained from Principal Components Analysis. The data used are ISAR images of armored vehicles gathered under a range of vehicle configurations allowing tests of both robustness and generality.
The problem we are addressing is one of generalization: given training data characterizing a set of targets (in specific configurations), how can we design a classifier that is robust to changes in target configuration and can generalize to other targets of the same generic class? The specific problem is identifying land vehicles from an inverse synthetic aperture radar image of the target. Issues in data modeling, experimental design and exploratory data analysis are discussed. Two complementary approaches are described: one that seeks to capture structure in the high- dimensional data space by projecting the data nonlinearly to a reduced dimensional feature space prior to classification; and a second that models the data in the data space using a Bayesian mixture model approach. Preliminary results for the mixture model approach are presented.
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