Patent Description:
There are many applications that require detecting the presence of, and if possible identifying, radioactive materials in target objects or regions. One such application is to prevent unauthorised passage of certain such materials across borders into nations or regions where such materials are prohibited. A suitable method in this border-monitoring application would be capable of performing the detection / identification as a vehicle passed through a detection zone, preferably without stopping in the zone, so as not to excessively impede the flow of traffic. Thus the method would preferably be capable of detecting the presence 'of prohibited materials rapidly, for example in a period of about <NUM> seconds or less. The method should preferably have high sensitivity, i.e. a low level of false negatives (failing to detect the presence of prohibited material) and high specificity, i.e. low false positives (signalling a detection when no prohibited material is present).

Detection of prohibited radionuclides is complicated by the fact that non-prohibited radionuclides, like prohibited radionuclides, may emit a certain level of ionising radiation, for example due to the presence of elevated concentrations of naturally occurring radioactive materials (NORMs), or of legitimate radiopharmaceutical products etc. Some existing systems, which use simple plastic scintillation detectors, measure only the gross level of radiation, in the form of gamma rays, emitted by a target. Such systems are prone to a high rate of nuisance alarms if the threshold level of radiation detection is set too low or a high rate of false negatives if the threshold level of radiation detection is set too high. Also, such systems are unable to distinguish legitimately traded goods containing elevated concentrations of NORMs from illicit or inadvertent and unlicensed goods containing prohibited radioactive materials.

A second generation of systems, known as Spectroscopic Portal Monitors (SPMs), based on NaI and HPGe detectors, seek to acquire the gamma ray spectrum of the target. Such systems contain processing to compare the acquired gamma ray spectrum with the spectra of radionuclides of interest. 'The spectrum processing methods have included, but not been limited to, those based on peak detection and matching, artificial neural networks, response function fitting, template matching, and wavelets.

High resolution spectroscopic equipment of the type found in SPMs is very expensive and is subject to poor reliability in field deployment due to the challenging operating conditions. Lower resolution spectroscopic equipment is less expensive and more robust but yields poorer performance with respect to radionuclide detection, namely higher rates of both false positives and false negatives. One example of a known method for determining the presence or absence of anomalous radioactive materials is disclosed in <CIT>.

It is an object of the present invention to substantially overcome, or at least ameliorate, one or more disadvantages of existing arrangements.

In this specification, a "class" is taken to refer to a "set" of gamma ray spectra with a common property, such as being gamma ray spectra of one or more radionuclide sources of interest. "Sources of interest" includes the "null" source referred to herein as "background". "Reference" gamma ray spectra are gamma ray spectra that belong to a set or "library" of previously acquired spectra. A class may contain reference gamma ray spectra as well as other gamma ray spectra, such as the gamma ray spectrum acquired from the target. In this context, a "plurality" of classes is taken to refer to two or more classes, for example two, three, four.

One or more embodiments of the present invention will now be described, by way of an example only, with reference to the accompanying drawings wherein:.

The present disclosure is related to an earlier PCT application, numbered <CIT>, made by the present applicant and entitled "Anomaly detection of radiological signatures".

The presently disclosed approach to radionuclide detection and identification is to acquire reference gamma ray spectra of radionuclide sources and to compare an acquired gamma ray spectrum of the target (the target spectrum) with the reference spectra. If the target spectrum is determined to belong to a first class containing the reference gamma ray spectra of a radionuclide source of interest, the target is deemed to contain the radionuclide source of interest. The disclosed approach uses Fisher Linear Discriminant Analysis (FLDA) to determine whether the target spectrum belongs to the first class containing the reference gamma ray spectra of the radionuclide source of interest. Further, it enables this determination to be made rapidly, for example as the target is passing through a checkpoint. This enables rapid decisions to be made as to whether the target is acceptable and, for example, whether it should be permitted to pass through the checkpoint. Approaches employing principal component analysis (PCA) produce loading coefficients ordered in terms of the highest variance in the data. Although the first few loading coefficients may explain a large proportion of the variation in the data, they may not represent the optimized separation between classes. The benefit of the FLDA technique is that it allows the determination of optimised loading coefficients, which maximise the separation between classes.

One feature of the disclosed approach is that each target spectrum may be pre-processed by functions that manipulate the spectrum in order to improve the classification performance. These functions may include, but not be limited to, intensity normalisation and spectrum standardisation.

The disclosed approach may include calibrating the device used for acquiring the gamma ray spectra. Over time the photopeaks of the spectra may drift, and calibration restores the correct energy values of the photopeaks. Calibration may be applied to the target gamma ray spectrum and/or to the reference gamma ray spectra. Calibration, either of the target gamma ray spectrum or of the reference gamma ray spectra, or of both, may be for the purpose of standardising the device used for acquiring the gamma ray spectra. Calibration may be conducted on a regular basis, for example, each time a spectrum is acquired, or every <NUM> spectra, or every <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM> or <NUM> spectra. Alternatively, calibration may be conducted at regular time intervals, for example every hour, or every <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM> or <NUM> hours.

A gamma ray spectrum may be acquired by a gamma ray detector. This may for example be a thallium-doped sodium iodide (NaI(T1))-based gamma ray detector. The gamma ray detector may alternatively be based on other materials such as High Purity Germanium (HPGe), Cadmium Telluride (CdTe), Cadmium Zinc Telluride (CZT) and Lanthanum Bromide (LaBr). A NaI(Tl)-based detector may be used in a thallium-doped sodium iodide based spectroscopic radiation portal monitor (RPM) in a border-monitoring application. The NaI(T1) based detector maybe used in a handheld configuration, backpack Configuration or some other portable configuration of the disclosed radionuclide detection system. The raw acquired gamma ray signals (either of the target or of reference samples) may be passed to a signal amplifier for amplifying the signals. The (amplified) gamma ray signals (either of the target or of reference samples) may be passed to a multichannel analyser, which divides the signals into a number of bins (or energy ranges). The bin values are collectively referred to as a spectrum. The bins represent the smallest increment of energy interval of the gamma ray spectrum to which counts are attributed. Typically the multichannel analyser will generate values in about <NUM> data bins, although there may be more or fewer than this number depending on the analyser, for example between <NUM> and <NUM> bins, between <NUM> and <NUM> bins, between <NUM> and <NUM> bins, between <NUM> and <NUM> bins, between <NUM> and <NUM> bins, between <NUM> and <NUM> bins, or between <NUM> and <NUM> bins. The number of bins is advantageously equal to an integral power of two. Typically, the bins cover energy values in the range <NUM> keV to <NUM> keV, although these endpoints may be. greater or lesser depending on the analyser, for example <NUM> keV and <NUM> keV respectively. The range may be one of <NUM> keV to <NUM> keV, <NUM> keV to <NUM> keV, <NUM> keV to <NUM> keV, <NUM> keV to <NUM> keV, <NUM> keV to <NUM> keV, <NUM> keV to <NUM> keV, <NUM> keV to <NUM> keV, <NUM> keV to <NUM> keV and <NUM> keV to <NUM> keV.

The number of values in a spectrum may be reduced, i.e. the spectrum may be rebinned. Rebinning may improve the computational speed. In general, each interval, or bin, in the acquired spectrum has an identical width. Rebinning the spectrum may involve uniformly increasing the width of each energy bin, thereby decreasing the total number of bins over the full energy range and increasing the number of counts within the newly defined bins. However, rebinning is not necessarily limited to linear functions. The rebinned spectrum may contain non-uniform bin widths which may, for example, be proportional to the energy squared or to some other suitable function of energy. The energy bins at higher energies may be larger than the lower energy bins, in order to ensure that the higher energy bins have sufficient counts. The rebinned spectrum may also contain user-defined bin widths, which may vary over the energy range. The number of energy bins of the rebinned spectrum is the number of variables in each spectrum. The greater the number of variables in a spectrum, the greater the computational time of the processing method. The spectra may be rebinned according to different functions. This may enable spectra from different detectors (of the same type, e.g. NaI-based) to be combined. The rebinning of the reference spectra and the target spectrum may be such that all spectra use the same energy bins.

As mentioned above, the (rebinned) target spectrum may be pre-processed. Preprocessing may involve either or both of intensity normalisation and spectrum standardisation.

For intensity normalisation, a target spectrum is normalised by the value in the energy bin with the highest number of counts. Intensity normalisation removes the effects of the wide range- of detector acquisition times, which can occur for example at ports of entry in a border monitoring application, and the effect of variation of the speed of passage of a target through the detection zone of an RPM in a border monitoring application.

For spectrum standardisation, a target spectrum is translated and scaled to have zero mean and unit variance across all energy bins.

A training data library comprises reference gamma ray spectra which are acquired from known samples of radionuclide sources of interest. These sources may be naturally occurring radioactive materials (NORMs), or man-made radionuclides that are known to be benign (acceptable), or represent a threat (unacceptable). The reference spectra may also comprise mixtures of such radionuclides, shielded or masked radionuclides, and combinations thereof that represent a threat.

The reference gamma ray spectra in the training data library may have been preprocessed in a similar fashion to the target spectra. This may provide more meaningful comparisons with the target spectra.

The disclosed approach enables a relatively rapid determination of whether a target contains a particular radionuclide. In a border monitoring application, the target may be, or may be transported by, a person, a truck or a car or a train carriage or some other vehicle or part thereof. Thus if the disclosed method determines that the target contains the particular radionuclide, an output signal may be generated. If that radionuclide is anomalous<NUM>(of concern), an alarm may be activated. In some cases it may be useful to generate an output signal indicating that the target does not contain the particular radionuclide in the event that the method so determines. In some cases the generated output may indicate which of a group of radionuclides is present in the target. A suitable alarm may be activated in response to the presence of a particular radionuclide of interest, for example an audible alarm (e.g. a horn, siren or similar), a visual alarm (e.g. a light, optionally a flashing light), activation of a barrier (e.g. lowering a boom gate, raising road spikes, closing a gate) to prevent passage of the target, activation of an instruction to a driver of the target (e.g. illumination of a STOP sign, activation of audible instructions to said driver) or some other type of alarm. The generated signal may also be a logic state provided to another system for the purpose of recognising the signal and responding. More than one of these types of alarm may be activated. They may be activated simultaneously. They may be activated non-simultaneously. They may be activated sequentially. Thus the disclosed apparatus may comprise one or more of an audible alarm device, a visual alarm device and a physical alarm device such as an activatable barrier. The disclosed method correspondingly may comprise activating the activatable barrier when a target is identified as an anomalous radionuclide.

An alternative mode of operation is for a signal to be generated only when the target does not contain an anomalous radionuclide (i.e. only for normal or acceptable radionuclides). In this case an activatable barrier may be removed or retracted in response to the signal, allowing a vehicle carrying no anomalous materials to pass.

<FIG> is a block diagram of an apparatus <NUM> within which embodiments of the present invention may be practised. The detector <NUM> is a spectroscopic portal detector, e.g. a NaI(Tl) based detector, deployed to acquire a gamma ray spectrum from a target, e.g. a vehicle <NUM> passing through a detection zone <NUM>. The apparatus <NUM> may also comprise a reference detector <NUM> for acquiring reference spectra, although this may in some embodiments be omitted. In such embodiments, the main detector (detector <NUM>) is capable of acquiring both the reference spectra and the target spectra. For example, "background" reference spectra may be acquired when no target is within the detection zone <NUM>. If a reference detector <NUM> is used, it may be remote from the portal detector <NUM>. The reference detector <NUM>, if present, may be shielded from the detection zone <NUM>.

An amplifier <NUM> is coupled to detector <NUM> for amplifying data from detector <NUM>, and, if present, reference detector <NUM>. Amplifier <NUM> is in turn coupled to a multichannel analyser <NUM> for providing an initial binning of the amplified data from amplifier <NUM>. Multichannel analyser <NUM> is coupled to the memory <NUM> of computer system <NUM> so that spectra from the analyser <NUM> may be stored in the memory <NUM>. Memory <NUM> also contains a training data library of reference spectra. Memory <NUM> is coupled to a processor <NUM>, also part of the computer system <NUM>, for processing the data stored in the memory <NUM> in order to determine if the target contains a given radionuclide source. An output signal <NUM> is generated if the target <NUM> is determined to contain one or more anomalous radionuclides. The output signal <NUM> is a logic state provided to another system (not shown) that is configured to recognise the output signal <NUM> and take appropriate action, such as activating an alarm. The alarm may take one or more of the following forms, simultaneously or sequentially: visual output, (e.g. a light, optionally a flashing light, or illumination of a STOP sign); audible output, (e.g. a horn, siren or similar, or verbal instructions to the driver).

<FIG> is a block diagram of an alternative apparatus 1a within which the embodiments of the invention may be practised. The apparatus 1a is similar to the apparatus <NUM> of <FIG>, with the addition of an activatable barrier <NUM> that is able to prevent passage of vehicle <NUM> in the event that the target is determined to contain one or more anomalous radionuclides. The activatable barrier <NUM> is in a normally open state (i.e. in a state in which passage of the vehicle <NUM> is allowed), and activating the barrier <NUM> closes the barrier <NUM> so as to hinder or prevent passage of the vehicle <NUM>. In the apparatus 1a, the output signal <NUM> causes the barrier <NUM> to be activated to prevent passage of vehicle <NUM> through the detection zone <NUM>. The activation of the barrier <NUM> could take one or more of the following forms: lowering a boom gate; raising road spikes; closing a gate.

In operation, vehicle <NUM> passes through detection zone <NUM>. This typically does not involve vehicle <NUM> stopping its forward motion, and commonly takes about <NUM> to about <NUM> seconds. Detector <NUM> acquires gamma ray photons from vehicle <NUM> during this period and generates a resulting signal that is passed to amplifier <NUM>, which amplifies the signal. The amplified signal is then passed to multichannel analyser <NUM> which performs an initial binning of the amplified target signal and passes a binned target spectrum to memory <NUM> for storage. Detector <NUM> may also be used for acquiring reference spectra for use in creating the training data library. In any event, the reference spectra are pre-processed as described above for the target spectrum, and then stored in memory <NUM>.

Pre-processed spectra stored in memory <NUM> are processed by processor <NUM> as described below in order to obtain a decision criterion. Processor <NUM> then determines from this decision criterion whether the target contains an anomalous radionuclide and, if so, generates an output signal <NUM>. Appropriate action may then be taken in response to the output signal <NUM>, for example vehicle <NUM> may be diverted for further investigation, an alarm may be activated, or in the apparatus 1a of <FIG>, the activatable barrier <NUM> may be activated so as to prevent passage of vehicle <NUM>.

As mentioned above, an alternative mode of operation of the apparatus <NUM> or 1a is to generate the output signal <NUM> only when the target is determined not to contain an anomalous radionuclide. In this mode of operation, the activatable barrier <NUM> would normally be in a closed state (i.e. in a state in which passage of the vehicle <NUM> is prevented or hindered), and activating the barrier <NUM> opens the barrier so as to allow or facilitate passage of the vehicle <NUM>. The barrier <NUM> would be activated in response to the output signal <NUM>, allowing a vehicle <NUM> carrying no anomalous radionuclides to pass. Thus the operation of the apparatus 1a may comprise generating the output signal <NUM> to the barrier <NUM> which prevents or hinders passage of the vehicle <NUM> when the vehicle <NUM> is identified as containing anomalous radionuclides and which allows passage of the vehicle <NUM> when the vehicle <NUM> is identified as not containing an anomalous radionuclide.

The apparatus <NUM> may also comprise a camera or similar photographic recording device (not shown). Such a device may be used for recording images of all vehicles passing through the detection zone, or for recording images of vehicles passing through the detection zone only when an anomalous radionuclide source is detected. The camera may be used for transmitting to an operator an image of all vehicles passing through the detection zone, or for transmitting to said operator images of vehicles passing through the detection zone only when an anomalous source is detected. In this case, the signal from the camera may be transmitted to a video display for displaying the image(s) to the operator. The disclosed method may comprise detecting, and recording and/or transmitting, an image of the vehicle or of a part (e.g. a number plate thereof), either for each vehicle passing through the detection zone or for each vehicle passing through the detection zone which is identified as an anomalous source or as containing an anomalous source.

In acquiring the target gamma ray spectrum, the vehicle <NUM> passes through a detection zone <NUM>, over which the detector <NUM> is capable of acquiring the gamma ray spectrum. The vehicle <NUM> may pass through the detection zone <NUM> at a mean velocity of about <NUM> to about <NUM>/h, or about <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM> or <NUM> to <NUM>/h, e.g. about <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM> or <NUM>/h. The time for passage of the vehicle <NUM> through the detection zone <NUM> may be about <NUM> to about <NUM> seconds, or about <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM> or <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM> or <NUM> to <NUM> seconds, e.g. about <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM> or <NUM> seconds. The detection zone may be about <NUM> to about <NUM> metres long, or about <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM> or <NUM> to <NUM> metres, e.g. about <NUM>, <NUM>, <NUM>, <NUM> or <NUM> metres. The vehicle <NUM> may be a truck or a car or a train carriage or some other vehicle or part thereof.

The processing method carried out by the processor <NUM> of the computing system <NUM> makes use of Fisher Linear Discriminant Analysis (FLDA). For a set of N observations (x<NUM>, x<NUM>,. xN), each observation being a vector of length n, and for a two-class problem where each observation belongs to one of two classes co and c<NUM>, FLDA is formulated as follows.

The means of the two classes are labelled as µ<NUM> and µl and the class covariances as Σ<NUM> and Σ<NUM>. The within-class scatter matrix Sw is defined as <MAT> while the between-class scatter matrix SB is defined as <MAT> where N<NUM> is the number of observations in class c<NUM> and µ is the mean of all N observations.

Projection of each observation x using an n by <NUM> projection vector w of "loading coefficients" transforms the means of the two classes to scalars wTµ<NUM> and wTµ<NUM>. The within-class and between-class scatter matrices Sw and SB are respectively transformed to scalars wTSww and wTSBw.

The "separation" J between the two projected classes as a function of the projection vector w is defined as the ratio of the projected between-class scatter matrix to the projected within-class scatter matrix: <MAT>.

According to FLDA, the "optimal" projection vector wopt is the projection vector that maximises the separation J between the two projected classes. It may be shown that the optimal projection vector wopt is the generalised eigenvector of SB and Sw corresponding to the largest (non-infinite) generalised eigenvalue λ of SB and Sw. Provided Sw is non-singular, this is equivalent to finding the eigenvector of <MAT> corresponding to the largest (non-infinite) eigenvalue λ of <MAT>: <MAT>.

In one implementation, the following radionuclide sources are of interest: 24IAm, <NUM>Ba, <NUM>Co, <NUM>Co, I37Cs, Highly Enriched Uranium (HEU), <NUM>Np, Weapons Grade Plutonium (WGPu), <NUM>Th, <NUM>K, <NUM>Ra, and Depleted Uranium (DU), and the "background". Other implementations contain many more sources of interest, or may contain fewer sources of interest. Each source of interest is known to be either benign or anomalous. The training data library contains multiple reference spectra of each of the sources of interest: The reference spectra are acquired by the gamma ray detector(s). Each reference spectrum is pre-processed before storage in the training data library as described above.

In one implementation, each of a set of sources of interest represented in the training data library is taken in turn as the current source. The reference spectra in the training data library are allocated between two classes such that reference spectra corresponding to the current source are allocated to the first class (class <NUM>), and reference spectra corresponding to other sources are allocated to the second class (class <NUM>). The mean µi and covariance Σi of each class is determined, as are the within-class and between-class scatter matrices. Equation <NUM> is then used to determine the optimal projection vector wopi that maximises the separation J between the two projected classes. The determined optimal projection vector wopt is then stored in association with the current source.

To determine whether a target spectrum belongs in class <NUM> (the current source of interest) or class <NUM> (not the current source of interest), the pre-processed target spectrum x is projected by the optimal projection vector wopt associated with the current source of interest to obtain a projected target spectrum wToptx. A distance between the projected target spectrum and the projected class <NUM> is then computed. If the distance is greater than a threshold distance, the target spectrum is determined to belong to class <NUM>, and the target is deemed to contain a sample of the current source of interest. Otherwise, the target spectrum is determined to belong to class <NUM>.

After all the sources of interest have been considered, the output signal <NUM> is generated depending on whether the target was deemed to contain at least one anomalous source of interest.

<FIG> is a flow chart illustrating a method <NUM> of processing a training data library of reference spectra according to an unclaimed embodiment. In one implementation, the method <NUM> is carried once before processing any acquired target spectra, and is controlled in its execution by the processor <NUM> of the computing system <NUM> in concert with the memory <NUM>, as described below.

The method <NUM> starts at step <NUM>, where the processor <NUM> pre-processes each reference spectrum in the training data library, rebinned to <NUM> bins, as described above. At the following step <NUM>, the processor <NUM> chooses a source from the set of sources of interest that has not yet been chosen as a current source. Two classes, one containing only reference spectra corresponding to the current source (class <NUM>), and one containing reference spectra corresponding to all other sources (class <NUM>) are notionally constructed. At the following step <NUM>, the processor <NUM> computes the mean µi and covariance Σi of each class, and the within-class and between-class scatter matrices Sw and SB. Step <NUM> follows, at which the processor <NUM> determines the optimal projection vector (also referred to herein as the optimal loading coefficients) wopt that maximises the separation between the two classes using Equation <NUM>. The method <NUM> then proceeds to step <NUM>, at which the processor <NUM> stores the determined optimal loading coefficients wopt, and the mean µ<NUM> and covariance Σ<NUM> of class <NUM>, in association with the current source. The method <NUM> continues at step <NUM>, where the processor <NUM> determines whether there are any sources in the set of sources of interest that have not yet been chosen. If so ("Y"), the method <NUM> returns to step <NUM>. If not ("N"), the method <NUM> concludes at step <NUM>.

In a variation of the method <NUM>, the processor <NUM> performs the computations of steps <NUM> and <NUM> not on the whole of each reference spectrum, but on some portion of each reference spectrum known as the "region of interest". In one implementation, the region of interest is that portion of the spectrum surrounding the principal peak in the spectrum of the current source. For example, for a radionuclide with a principal peak at <NUM> keV, the region of interest is the range between <NUM> keV and <NUM> keV. In other implementations, the region of interest comprises multiple disjoint sections of each spectrum. At step <NUM> in the variation, the processor <NUM> stores the endpoints of the region of interest alongside the other parameters for the current source.

<FIG> is a flow chart illustrating a method <NUM> of processing a target spectrum according to an unclaimed embodiment. The method <NUM> is controlled in its execution by the processor <NUM> of the computing system <NUM> in concert with the memory <NUM>, as described below.

The method <NUM> starts at step <NUM>, where the processor <NUM> pre-processes the target spectrum, rebinned to <NUM> bins, as described above. At the following step <NUM>, the processor <NUM> chooses a source from the set of sources of interest that has not yet been chosen as a current source. At the following step <NUM>, the processor <NUM> loads the optimal loading coefficients wopt and the mean µ<NUM> and covariance Σl of class <NUM> associated with the current source that were stored at step <NUM> of the method <NUM>. Step <NUM> follows, at which the processor <NUM> determines whether the target spectrum belongs in class <NUM> (the current source of interest) or class <NUM> (not the current source of interest) using the optimal loading coefficients wopt. If the target spectrum is determined to belong to class <NUM>, the target is deemed to contain a sample of the current source.

One implementation of step <NUM> is for the processor <NUM> to compute the Mahalanobis distance D between the projected target spectrum wToptx and the projected class <NUM>, using the mean µi and covariance Σ<NUM> of class <NUM> that were stored in association with the current source at step <NUM> of the method <NUM>: <MAT>.

The target spectrum x is then determined to belong to class <NUM> if the Mahalanobis distance D is above a threshold distance from the projected class <NUM>.

In an alternative implementation of step <NUM>, the processor <NUM> computes the Euclidean distance d between the projected target spectrum wToptx and the projected class. <NUM> using the mean µ<NUM> of class <NUM>: <MAT>.

The target spectrum x is then determined to belong to class <NUM> if the Euclidean distance d is above a threshold distance from the projected class <NUM>.

The threshold distance used in step <NUM> may be obtained from the training data library or a separate set of reference spectra from class <NUM>. In one implementation of step <NUM>, the threshold distance is defined by a power law relationship with the mean gross counts in class <NUM>, i.e. the mean of the sum of all counts over all bins of all reference spectra in class <NUM>. For example, the standard deviation y of a class may be related to the mean gross counts g by the following equation: <MAT>.

In Equation <NUM>, A is a positive number and B is a number between <NUM> and <NUM>. For class <NUM>, B is typically around <NUM>. For class <NUM>, B is typically between <NUM> and <NUM>. In this implementation, the threshold distance is set at a number, typically between one and ten, for example five, of standard deviations of class <NUM>. In another implementation of step <NUM>, the threshold distance is a user defined value.

The method <NUM> continues at step <NUM>, where the processor <NUM> determines whether there are any sources in the set of sources of interest that have not yet been chosen. If so ("Y"), the method <NUM> returns to step <NUM>. If not ("N"), the processor <NUM> at step <NUM> generates the output signal <NUM> depending on the determined contents of the target as described above. The method <NUM> then concludes. The method <NUM> may be categorised as an "identification" method.

If the variation of the method <NUM> was used to process the reference spectra, then a complementary variation of the method <NUM> loads the endpoints of the region of interest associated with the current source at step <NUM> along with the optimal loading coefficients and other parameters associated with the current source, and performs the computations of step <NUM> within the "region of interest".

If one of the sources of interest is "background", and the method <NUM> does not result in an indication that the target contains any of the sources of interest, this result may be taken as an indication that the target contains a radionuclide source that is not presently in the training data library.

In an unclaimed alternative embodiment, the two classes for FLDA purposes are predefined by a user. In one example, the first class comprises reference spectra corresponding to Special Nuclear Materials (fissile radionuclides) and the second class comprises reference spectra corresponding to NORMs. To process the training data library according to the alternative embodiment, the method <NUM> may be used, except that two classes are formed at step <NUM> in accordance with the user definition. Steps <NUM>, <NUM>, and <NUM> are only performed once, and step <NUM> is not needed. To process the target spectrum according to the alternative embodiment, the method <NUM> may be used, except that the steps <NUM> and <NUM> are only performed once, and there is no need for the steps <NUM> and <NUM>. In the example, the result of the target spectrum processing under the alternative embodiment is a signal indicating whether a target spectrum is a Special Nuclear Material or a NORM. This alternative embodiment may be categorised as a "classification" or "anomaly detection" method.

A second example (according to the claimed invention) is similar to the first example, except that the first class (corresponding to a threat) comprises at least one "artificial" gamma ray spectrum. The artificial spectrum in one implementation is a constant value with an additive Gaussian noise component. In another implementation, the artificial spectrum is a quasi-linear spectrum with an additive Gaussian noise component. The second class comprises reference spectra corresponding to NORMs. The second example has the advantage over the first example that no prior knowledge of the threat (or non-NORM) spectra is needed in order to conclude that a target contains a threat under a wide range of conditions such as different intensities or shielding materials.

Fisher linear discriminant analysis as formulated above may readily be generalised from a two-class problem to a multi-class problem. In a further example of the alternative embodiment, each class is defined to contain only reference spectra corresponding to a unique radionuclide. The number of classes can then be as many as the number of radionuclides represented in the training data library. Step <NUM> then returns the number of the class in which the target spectrum is determined to belong.

If the target is deemed to contain a sample of the current source of interest, the distance of the target spectrum from the projected class <NUM>, computed in step <NUM> of the method <NUM>, shows a relationship with the intensity of the current source of interest within the target that is approximately linear over small values of intensity, and tends to logarithmic over larger values of intensity. The computed distance of the target spectrum from class <NUM> may therefore be used in an optional processing step in the method <NUM> to estimate the intensity of the current source of interest within the target. The standard deviation y computed from the mean gross counts in class <NUM> using Equation <NUM> may be used to provide an error estimate for the estimated intensity of the current source of interest within the target.

<FIG> and <FIG> collectively form a schematic block diagram of a general purpose computer system <NUM>, which may be used as the computing system <NUM> in the apparatus <NUM> of <FIG> or the apparatus 1a of <FIG>, to carry out the processing methods <NUM> and <NUM> of <FIG> and <FIG>.

As seen in <FIG>, the computer system <NUM> is formed by a computer module <NUM>, input devices such as a keyboard <NUM>, a mouse pointer device <NUM>, a scanner <NUM>, a camera <NUM>, and a microphone <NUM>, and output devices including a printer <NUM>, a display device <NUM> and loudspeakers <NUM>. An external Modulator-Demodulator (Modem) transceiver device <NUM> may be used by the computer module <NUM> for communicating to and from a communications network <NUM> via a connection <NUM>. The network <NUM> may be a wide-area network (WAN), such as the Internet or a private WAN. Where the connection <NUM> is a telephone line, the modem <NUM> may be a traditional "dial-up" modem. Alternatively, where the connection <NUM> is a high capacity (e.g. cable) connection, the modem <NUM> may be a broadband modem. A wireless modem may also be used for wireless connection to the network <NUM>.

The computer module <NUM> typically includes at least one processor unit <NUM>, and a memory unit <NUM> for example formed from semiconductor random access memory (RAM) and semiconductor read only memory (ROM). The memory unit <NUM> may be identified with the memory <NUM> of the computer system <NUM>, and the processor unit <NUM> may be identified with the processor <NUM> of the computer system <NUM>.

The module <NUM> also includes an number of input/output (I/O) interfaces including an audio-video interface <NUM> that couples to the video display <NUM>, loudspeakers <NUM> and microphone <NUM>, an I/O interface <NUM> for the keyboard <NUM>, mouse <NUM>, scanner <NUM>, camera <NUM> and optionally a joystick (not illustrated), and an interface <NUM> for the external modem <NUM> and printer <NUM>. In some implementations, the modem <NUM> may be incorporated within the computer module <NUM>, for example within the interface <NUM>. The computer module <NUM> also has a local network, interface <NUM> which, via a connection <NUM>, permits coupling of the computer system <NUM> to a local computer network <NUM>, known as a Local Area Network (LAN). As also illustrated, the local network <NUM> may also couple to the wide network <NUM> via a-connection <NUM>, which would typically include a so-called "firewall" device or device of similar functionality. The interface <NUM> may be formed by an EthernetTM circuit card, a BluetoothTM wireless arrangement or an IEEE <NUM> wireless arrangement.

The interfaces <NUM> and <NUM> may afford either or both of serial and parallel connectivity, the former typically being implemented according to the Universal Serial Bus (USB) standards and having corresponding USB connectors (not illustrated). Storage devices <NUM> are provided and typically include a hard disk drive (HDD) <NUM>. Other storage devices such as a floppy disk drive and a magnetic tape drive (not illustrated) may also be used. An optical disk drive <NUM> is typically provided to act as a non-volatile source of data. Portable memory devices, such optical disks (e.g. CD-ROM, DVD), USB-RAM, and floppy disks for example may then be used as appropriate sources of data to the system <NUM>.

The components <NUM> to <NUM> of the computer module <NUM> typically communicate via an interconnected bus <NUM> and in a manner which results in a conventional mode of operation of the computer system <NUM> known to those in the relevant art. Examples of computers on which the described arrangements can be practised include IBM-PC's and compatibles, Sun Sparcstations, Apple MacTM or alike computer systems evolved therefrom.

The methods <NUM> and <NUM>, described above, may be implemented as one or more software application programs <NUM> executable within the computer system <NUM>. In particular, the steps of the methods <NUM> and <NUM> are effected by instructions <NUM> in the software <NUM> that are carried out within the computer system <NUM>. The software instructions <NUM> may be formed as one or more code modules, each for performing one or more particular tasks. The software may also be divided into two separate parts, in which a first part and the corresponding code modules performs the methods <NUM> and <NUM> and a second part and the corresponding code modules manage a user interface between the first part·and the user.

The software <NUM> is generally loaded into the computer system <NUM> from a computer readable medium, and is then typically stored in the HDD <NUM>, as illustrated in <FIG>, or the memory <NUM>, after which the software <NUM> can be executed by the computer system <NUM>. In some instances, the application programs <NUM> may be supplied to the user encoded on one or more CD-ROM <NUM> and read via the corresponding drive <NUM> prior to storage in the memory <NUM> or <NUM>. Alternatively the software <NUM> may be read by the computer system <NUM> from the networks <NUM> or <NUM> or loaded into the computer system <NUM> from other computer readable media. Additionally or alternatively, data, for example the training data library or reference spectra used in preparing the training data library, may be stored in the memory <NUM> or <NUM> or may be loaded into said memory from a CD or other computer readable medium, or over the internet or by some other means. Computer readable storage media refers to any storage medium that participates in providing instructions and/or data to the computer system <NUM> for execution and/or processing. Examples of such storage media include floppy disks, magnetic tape, CD-ROM, a hard disk drive, a ROM or integrated circuit, USB memory, a magneto-optical disk, or a computer readable card such as a PCMCIA card and the like, whether or not such devices are internal or external of the computer module <NUM>. Examples of computer readable transmission media that may also participate in the provision of software, application programs, instructions and/or data to the computer module <NUM> include radio or infra-red transmission channels as well as a network connection to another computer or networked device, and the Internet or Intranets including e-mail transmissions and information recorded on Websites and the like.

The second part of the application programs <NUM> and the corresponding code modules mentioned above may be executed to implement one or more graphical user interfaces (GUIs) to be rendered or otherwise represented upon the display <NUM>. Through manipulation of typically the keyboard <NUM> and the mouse <NUM>, a user of the computer system <NUM> and the application may manipulate the interface in a functionally adaptable manner to provide controlling commands and/or input to the applications associated with the GUI(s). Other forms of functionally adaptable user interfaces may also be implemented, such as an audio interface utilizing speech prompts output via the loudspeakers <NUM> and user voice commands input via the microphone <NUM>.

<FIG> is a detailed schematic block diagram of the processor <NUM> and a "memory" <NUM>. The memory <NUM> represents a logical aggregation of all the memory devices (including the HDD <NUM> and semiconductor memory <NUM>) that can be accessed by the computer module <NUM> in <FIG>.

When the computer module <NUM> is initially powered up, a power-on self-test (POST) program <NUM> executes. The POST program <NUM> is typically stored in a ROM <NUM> of the semiconductor memory <NUM>. A program permanently stored in a hardware device such as the ROM <NUM> is sometimes referred to as firmware. The POST program <NUM> examines hardware within the computer module <NUM> to ensure proper functioning, and typically checks the processor <NUM>, the memory (<NUM>, <NUM>), and a basic input-output systems software (BIOS) module <NUM>, also typically stored in the ROM <NUM>, for correct operation. Once the POST program <NUM> has run successfully, the BIOS <NUM> activates the hard disk drive <NUM>. Activation of the hard disk drive <NUM> causes a bootstrap loader program <NUM> that is resident on the hard disk drive <NUM> to execute via the processor <NUM>. This loads an operating system <NUM> into the RAM memory <NUM> upon which the operating system <NUM> commences operation. The operating system <NUM> is a system level application, executable by the processor <NUM>, to fulfil various high level functions, including processor management, memory management, device management, storage management, software application interface, and generic user interface.

The operating system <NUM> manages the memory (<NUM>, <NUM>) in order to ensure that each process or application running on the computer module <NUM> has sufficient memory in which to execute without colliding with memory allocated to another process. Furthermore, the different types of memory available in the system <NUM> must be used properly so that each process can run effectively. Accordingly, the aggregated memory <NUM> is not intended to illustrate how particular segments of memory are allocated (unless otherwise stated), but rather to provide a general view of the memory accessible by the computer system <NUM> and how such is used.

The processor <NUM> includes a number of functional modules including a control unit <NUM>, an arithmetic logic unit (ALU) <NUM>, and a local or internal memory <NUM>, sometimes called a cache memory. The cache memory <NUM> typically includes a number of storage registers <NUM> - <NUM> in a register section. One or more internal buses <NUM> functionally interconnect these functional modules. The processor <NUM> typically also has one or more interfaces <NUM> for communicating with external devices via the system bus <NUM>, using a connection <NUM>.

The application program <NUM> includes a sequence of instructions <NUM> that may include conditional branch and loop instructions. The program <NUM> may also include data <NUM> which is used in execution of the program <NUM>. The instructions <NUM> and the data <NUM> are stored in memory locations <NUM>-<NUM> and <NUM>-<NUM> respectively. Depending upon the relative size of the instructions <NUM> and the memory locations <NUM>-<NUM>, a particular instruction may be stored in a single memory location as depicted by the instruction shown in the memory location <NUM>. Alternately, an instruction may be segmented into a number of parts each of which is stored in a separate memory location, as depicted by the instruction segments shown in the memory locations <NUM>-<NUM>.

In general, the processor <NUM> is given a set of instructions which are executed therein. The processor <NUM> then waits for a subsequent input, to which it reacts to by executing another set of instructions. Each input may be provided from one or more of a number of sources, including data generated by one or more of the input devices <NUM>, <NUM>, data received from an external source across one of the networks <NUM>, <NUM>, data retrieved from one of the storage devices <NUM>, <NUM> or data retrieved from a storage medium <NUM> inserted into the corresponding reader <NUM>. The execution of a set of the instructions may in some cases result in output of data. Execution may also involve storing data or variables to the memory <NUM>.

The disclosed arrangements use input variables <NUM>, that are stored in the memory <NUM> in corresponding memory locations <NUM>-<NUM>. The arrangements produce output variables <NUM>, that are stored in the memory <NUM> in corresponding memory locations <NUM>-<NUM>. Intermediate variables <NUM> may be stored in memory locations <NUM>, <NUM>, <NUM> and <NUM>.

The register section <NUM>-<NUM>, the arithmetic logic unit (ALU) <NUM>, and the control unit <NUM> of the processor <NUM> work together to perform sequences of micro-operations needed to perform "fetch, decode, and execute" cycles for every instruction in the instruction set making up the program <NUM>. Each fetch, decode, and execute cycle comprises:.

Thereafter, a further fetch, decode, and execute cycle for the next instruction may be executed. Similarly, a store cycle may be performed by which the control unit <NUM> stores or writes a value to a memory location <NUM>.

Each step or sub-process in the processes of <FIG> is associated with one or more segments of the program <NUM>, and is performed by the register section <NUM>-<NUM>, the ALU <NUM>, and the control unit <NUM> in the processor <NUM> working together to perform the fetch, decode, and execute cycles for every instruction in the instruction set for the noted segments of the program <NUM>.

The methods <NUM> and <NUM> may alternatively be implemented in dedicated hardware such as one or more integrated circuits performing the functions or sub functions of the method. Such dedicated hardware may include graphic processors, digital signal processors, Field Programmable Gate Arrays (FPGA's) or one or more microprocessors and associated memories.

The foregoing describes only some embodiments of the present invention, and modifications and/or changes can be made thereto without departing from the scope of the invention, the embodiments being illustrative and not restrictive.

Claim 1:
A method of detecting and identifying radioactive materials by processing a gamma ray spectrum acquired from a target, the method comprising:
acquiring the gamma ray spectrum from the target as the target passes through a detection zone;
determining whether the gamma ray spectrum of the target belongs to a first class or to a second class of gamma ray spectra using optimal loading coefficients, wherein the optimal loading coefficients have been obtained using Fisher linear discriminant analysis by:
computing a between-class scatter matrix and a within-class scatter matrix for the gamma ray spectra in the first class and the second class; and
computing the optimal loading coefficients from the generalized eigenvector corresponding to the largest generalized eigenvalue of the between-class and within-class scatter matrices,
wherein the determining whether the gamma ray spectrum of the target belongs to the first class or to the second class further comprises:
- computing a distance between the gamma ray spectrum of the target projected by the optimal loading coefficients and the gamma ray spectrum of the second class projected by the optimal loading coefficients; and
- determining that the gamma ray spectrum of the target belongs to the first class when the computed distance is above a threshold distance;
wherein the first class comprises at least one artificial gamma ray spectrum being one of
(i) a constant value with an additive Gaussian noise component, and
(ii) a quasi-linear spectrum with an additive Gaussian noise component
wherein the second class comprises gamma ray spectra of naturally occurring radioactive materials, NORMs; and
generating an output signal dependent on the determining.