Patent Publication Number: US-2005128069-A1

Title: System and method for detecting contraband

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS  
      The present patent application claims priority from provisional patent application No. 60/519,727 filed on Nov. 12, 2003, and U.S. patent application Ser. No. 10/838,968 filed on May 4, 2004, which are incorporated herein by reference in their entirety. 
    
    
     BACKGROUND OF THE INVENTION  
      1). Field of the Invention  
      This invention relates to a system and method for detecting contraband.  
      2). Discussion of Related Art  
      In recent years, the detection of contraband, such as explosives, being transported in luggage and taken onto various means of transportation has become increasingly important. Advanced Explosive Detection Systems (EDSs) have been developed that can not only see the shapes of the articles being carried in the luggage but can also determine whether or not the articles contain explosive materials.  
      These detection systems include computed tomography (CT) machines. There are also explosive detection devices (EDDs) based on other technologies such as quadrapole resonance (QR). EDDs differ from EDSs in that the former cannot find the whole range of explosives as specified by the Transportation Security Administration (TSA). The EDDs and/or EDSs are typically manufactured by different companies and compute results in a way unrelated to each other.  
      To improve the performance of explosive detection systems, one approach is to combine multiple systems. In order to fuse the data from the different systems in a meaningful way, a tedious process of collecting joint data, designing a tailored data fusion algorithm, and subsequently tuning this algorithm is required. Additionally, in order for one to be able to accomplish this, he or she may have a very intimate knowledge of how each of the EDDs and EDSs work.  
     SUMMARY OF THE INVENTION  
      The invention provides a method that can be adopted by existing and new systems for detecting contraband. In this method, a system, or risk assessment agent, comprises a contraband detection apparatus or another computerized processor that assesses risk. The risk assessment agent will receive input data in the form of risk values, each risk value being indicative of the presence of a particular type of contraband. Furthermore, the system will utilize its risk assessment (i.e., scanning results) to modify the risk values according to a given calculus and provide these modified risk values on output.  
      Because the calculus is an objective standard based on probability theory, the risk values will be a common language that allows systems to work together without knowing about one another. When two systems are joined together, the second system will use the output risk values of the first system as input risk values. This is thus a form of decentralized or distributed data fusion where there is no central data fusion entity.  
      The invention provides a method for detecting contraband, comprising scanning a container with a first type of contraband detection apparatus, based on results of said scanning with the first type of contraband detection apparatus, generating a plurality of preliminary risk values, each preliminary risk value indicative of the presence of a respective type of contraband, scanning the container with a second type of contraband detection apparatus, and based on results of said scanning with the second type of contraband detection apparatus, modifying the preliminary risk values to generate a plurality of final risk values, each final risk value corresponding to a respective one of the preliminary risk values and indicative of the presence of a respective type of contraband.  
      The risk values may be on a scale from 0 to 100 percent, or 1 to 99 percent.  
      The risk calculus may be Bayesian probability theory wherein the initial risk values are prior probabilities of the presence of each type of contraband, the probabilities are modified using Bayes&#39; rule with the likelihood of the scanning results given the presence of the various contraband types, and the output probabilities are the posterior probabilities.  
      Other calculi such as Dempster-Schafer theory can also yield equivalent results. A strength of using Bayesian probability is its simplicity, which is an advantage when applying the method as a standard.  
      The decentralized data fusion relies on the assumption that the systems are orthogonal or near-orthogonal, i.e., conditionally independent. This is usually fulfilled when technologies are used that measure different physical properties or independent sources of information.  
      The method may further comprise entering information of a person taking the container for loading into a loading bay of a craft and based on the information, generating a personal risk value, said generating of the plurality of intermediate risk values being based on the personal risk value and the results of said scanning with the first type of contraband detection apparatus.  
      The method may further be extended to a risk assessment agent that uses non-sensor information such as passenger information or a general threat alert state to modify the risk values.  
      The method may further comprise triggering an alarm based on at least one of the final risk values.  
      The first type of contraband detection apparatus may be a CT scanner, and the second type of contraband detection apparatus may be a QR scanner. The scanning with the CT scanner may take place before said scanning with the QR scanner.  
      The invention also provides a method for detecting contraband comprising entering information of a person taking a container for loading into a loading bay of a craft, based on the information, generating a personal risk value, scanning the container with a first contraband detection apparatus, and based on the personal risk value and results of said scanning, generating at least a preliminary risk value.  
      The method may further comprise scanning the container with a second contraband detection apparatus and based on results of said scanning with the second contraband detection apparatus, modifying the preliminary risk value to generate a final risk value.  
      The method may further comprise triggering an alarm based on the final risk value.  
      The first contraband detection apparatus may be a CT scanner, and the second contraband detection apparatus may be a QR scanner. The scanning with the CT scanner may take place before said scanning with the QR scanner.  
      The invention further provides a method for detecting contraband comprising scanning a container with a first contraband detection apparatus, based on results of said scanning with the first contraband detection apparatus, generating an preliminary risk value on a scale from 1 to 99 percent, scanning the container with a second contraband detection apparatus, and based on the preliminary risk value and results of said scanning with the second contraband detection apparatus, generating a final risk value.  
      The method may further comprise entering information of a person taking a container for loading into a loading bay of a craft and based on the information, generating a personal risk value, said generating of the preliminary risk value being based on the personal risk value and the results of said scanning with the first contraband detection apparatus.  
      The method may further comprise triggering an alarm based on the final risk value.  
      The first contraband detection apparatus may be a CT scanner, and the second contraband detection apparatus may be a QR scanner. The scanning with the CT scanner may take place before said scanning with the QR scanner.  
      The invention further provides a method for detecting contraband comprising scanning a container with a first contraband detection apparatus, based on results of said scanning with the first contraband detection apparatus, generating a plurality of preliminary risk values, each preliminary risk value corresponding to a particular type of contraband, scanning the container with a second contraband detection apparatus, and based on the preliminary risk values and results of said scanning with the second contraband detection apparatus, generating a plurality of final risk values, each final risk value corresponding to a particular type of contraband.  
      The invention further provides a system for detecting contraband comprising a contraband detection apparatus to scan a container for contraband and a computer connected to the contraband detection apparatus to generate a personal risk value based information of a person taking the container for loading into a loading bay of a craft and at least a preliminary risk value based on the personal risk value and results of said scanning.  
      The system may further comprise a second contraband detection apparatus to scan the container for contraband.  
      The first contraband detection apparatus may be a CT scanner, and the second contraband detection apparatus may be a QR scanner.  
      The system may further comprise a transportation subsystem interconnecting the CT scanner and the QR scanner to transport the container between the CT scanner and the QR scanner.  
      The invention may further provide a system for detecting contraband comprising a first contraband detection apparatus to a perform a first scan on a container for contraband, a second contraband detection apparatus to perform a second scan on the container for contraband, and a computer connected to the first and second detection apparatuses to generate an preliminary risk value on a scale from 1 to 99 percent based on results of the first scan and a final risk value based on the preliminary risk value and results of the second scan.  
      The invention may further provide a system for detecting contraband comprising a first contraband detection apparatus to perform a first scan on a container for contraband, a second contraband detection apparatus to perform a second scan on the container for contraband, and a computer connected to the first and second detection apparatuses to generate a plurality of preliminary risk values based on results of the first scan, each preliminary risk value corresponding to a particular type of contraband, and a plurality of final risk values based on the preliminary risk values and results of the second scan, each final risk value corresponding to a particular type of contraband.  
      The invention may further provide a system for detecting contraband in a container comprising a risk assessment agent that accepts as input data a plurality of risk values, each risk value indicative of the presence of a respective type of contraband, said risk assessment agents modifying the risk values, based on its own risk assessment applying an empirical or expert based quantification of its risk assessment within a specified risk calculus and said agent outputting the said modified risk values.  
      The risk assessment agent may be a virtual agent residing outside of a physical risk assessment unit. The risk assessment agent may incorporate sensor data for a container. The risk assessment agent may be embedded in a contraband detection apparatus scanning the container. The risk assessment agent may apply an assessment of the general threat state.  
      The risk assessment agent may be a passenger profiling screening system assessing the relative risk of an individual to whom the container belongs.  
      The risk values may be probabilities, with values between 0 and 1. The sum of the probabilities of each threat category and the probability of no threat may be 1.  
      The risk calculus may be Bayesian probability, and the likelihood of the observation given to the various threat categories is used.  
      Multiple risk assessment agents may be combined in a series, each using the previous agent&#39;s risk value output as risk value input. The system may provide decentralized data fusion.  
      A decision to alarm or not may be based on the output threat values. A decision whether to send the container to another risk assessment agent may be based on the output threat state. The decision whether to alarm or not may be based on whether the sum of the risk values exceeds a threshold.  
      The contraband detection apparatus may be a CT scanner or a QR scanner.  
      The invention also provides a method of detecting contraband, including storing data representing a first distribution of reference values measured when scanning reference objects having a first predetermined feature, scanning an inspected object to measure a variable of the inspected object, locating the variable among the reference values, and determining a score of the data representing the first distribution corresponding to the variable, as an indication of the likelihood that the inspected object has the first predetermined feature.  
      The data representing the first distribution may be a function, approximating the first distribution, of score against reference values of the first distribution.  
      The method may further include storing data representing a second distribution of reference values measured when scanning reference objects having the second predetermined feature, and determining a score of the data representing the second distribution corresponding to the variable, as an indicator of the likelihood that the inspected object has the second predetermined feature.  
      The method may further include storing data representing a second distribution of reference values measured when scanning reference objects without the first predetermined feature, and determining a score of the data representing the second distribution corresponding to the variable as an indicator of the likelihood that the inspected object is without the first predetermined feature.  
      The method may further include normalizing the score from the data representing the first and second distributions, the normalized score from the data representing the first distribution being used to indicate the likelihood that the object has the predetermined feature.  
      The inspected object may be a container such as luggage. Alternatively, the inspected object may be located in a container.  
      The invention also provides a computer-readable medium having stored thereon instructions which, when executed by at least one processor, detects contraband according to the method including storing data representing a first distribution of reference values measured when scanning reference objects having a first predetermined feature, storing a variable measured when scanning an object, locating the variable among the reference values, and determining a score of the data representing the first distribution corresponding to the variable, as an indication of the likelihood that the inspected object has the first predetermined feature.  
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS  
      The invention is described by way of example with reference to the accompanying drawings, wherein:  
       FIG. 1  is a schematic of a contraband detection system, including a scanning subsystem and a computer subsystem, including a database;  
       FIG. 2  is a schematic of the computer subsystem;  
       FIG. 3  is a table illustrating the use of the database;  
       FIGS. 4A-4C  are schematics of the contraband detection system, illustrating generation of a prior threat state before a container enters the scanning subsystem ( FIG. 4A ) and modification of the threat state as the container passes through the scanning subsystem (FIGS.  4 B and  4 C);  
       FIG. 5  is a flow chart illustrating use of the contraband detection system;  
       FIG. 6  is a schematic of a scanning subsystem;  
       FIG. 7  is a graph illustrating an example of histograms and probability distributions for feature X 1  for bags with and without explosive type 2 (B 2 ) using gamma distribution;  
       FIG. 8  is a graph illustrating the probability of bomb type B 2  given the measurement X 1 ;  
       FIG. 9  are graphs illustrating histograms of class data and modeled gaussians for distributions of reference values measured when scanning reference objects with and without predetermine features; and  
       FIG. 10  are graphs representing the analysis of a measured variable to determine the threat state of a scanned object.  
    
    
     DETAILED DESCRIPTION OF THE INVENTION  
       FIG. 1  illustrates a contraband detection system  10 , or EDS, including a scanning subsystem  12  and a computer subsystem  14 .  
      The scanning subsystem  12  includes a first contraband detection apparatus  16 , a second contraband detection apparatus  18 , and a conveyor belt  20 .  
      The first contraband detection apparatus  16 , or EDS, is a CT scanner (hereinafter referred to as “CT scanner  16 ”). Although not illustrated in detail, the CT scanner  16  includes a gantry support with a tubular passageway therethrough and a gantry mounted to the gantry support to be rotated around the passageway. An X-ray source and X-ray detectors are secured to diametrically opposing sides of the gantry. The tubular passageway is sized appropriately to allow various cargo containers, such as suitcases and other types of luggage, to pass though the CT scanner  16 .  
      The second contraband detection apparatus  18  is a QR scanner (hereinafter referred to as “QR scanner  18 ”). Although not shown in detail, the QR scanner  18  has a structure that is similar to CT scanner  16  and has a tubular passageway, similar in size to the passageway on the CT scanner  16 , therethrough; however, the components include a quadrupole resonance transmitter and a receiver. It is not necessary for the components of to be moveable within the QR scanner  18 , but the components must be directed toward the passageway through the QR scanner  18 .  
      The conveyor belt  20  interconnects the CT scanner  16  and QR scanner  18  contraband detection apparatuses and passes through the passageways on both the CT scanner  16  and the QR scanner  18 .  
      Referring to  FIGS. 1 and 2 , the computer subsystem  14  includes a computer  22 , and an electronic database  26  which is connected to the computer  22 . The computer  22  includes a processor  100 , a main memory  102 , a static memory  104 , a network interface device  106 , a video display device  108 , an alpha-numeric input device  110 , a cursor control device  112 , a drive unit  114  including a machine-readable medium  116 , and a signal generation device  118 . All of the components of the computer subsystem  14  are interconnected by a bus  120 . The computer subsystem  14  is connected to a network  122  through the network interface device  106 . Although illustrated as containing both the database  26  and the static memory  104  within the computer  22 , the computer subsystem  14  may only contain one or the other.  
      The machine-readable medium  116  includes a set of instructions  124 , which may be partially transferred to the processor  100  and the main memory  102  through the bus  120 . Although not illustrated, the processor  100  and the main memory  102  may also have separate internal sets of instructions.  
      As illustrated in  FIG. 3 , the database  26 , and/or the static memory  104 , includes a list of characteristics about various types of people, such as credit card information, nationality, and whether or not they have a one-way airline ticket, and a list of corresponding risk levels or threat states. The risk values may be expressed as numerical probability scales with upper and lower limits such as probabilities from 0 to 1, or 0.01 to 0.99, or percentages from 0 to 100 percent, or 1 to 99 percent (or any ranges in between, such as 2 to 98 percent or 0.02 to 0.98). The risk values are associated with each type of person and the likelihood of that person attempting to carry an explosive device, or other contraband, onto the plane.  
      The computer  22  is connected to both the CT scanner  16  and the QR scanner  18  and is programmed with a Threat State Propagation (TSP) protocol common to both types of scanners. The TSP protocol is an embodiment of the described invention. Although not illustrated, it should be understood that the system  10  also includes an alarm that is connected to the computer  22 .  
      The TSP protocol allows the system  10  to decide whether or not any given bag contains contraband, such as a bomb, and trigger the alarm, or simply clear the bag for passage.  
      The case that a bag contains an explosive device of category i, i=1, . . . ,n is denoted B i , and the case that it does not contain any contraband, B 0 . The event of an alarm is denoted as A 1 , and the event of a clear as A 0 . The probability of detection, Pd, and the probability of false alarm, Pfa, can be written as conditional probabilities: 
 
 Pd   i   =P ( A   1   |B   i )  (1) 
 
 Pfa=P ( A   1   |B   0 ) 
 
      The probabilities in Eq. 1 describe the expected machine decision when only the underlying truth, whether or not there is actually a bomb in the bag, is known. These probabilities are also called likelihoods.  
      In a real-life operational situation, the truth is not known but the machine decision is known. To quantify the probability that a bag has a bomb given the system  10  decision (alarm or clear), Bayes&#39; rule can be used:  
                 P   ⁡     (       B   i     ❘     A   j       )       =         P   ⁡     (       A   j     ❘     B   i       )       ⁢     P   ⁡     (     B   i     )                     P   ⁡     (       A   j     ❘     B   1       )       ⁢     P   ⁡     (     B   1     )         +       P   ⁡     (       A   j     ❘     B   2       )       ⁢     P   ⁡     (     B   2     )         +   …   +                 P   ⁢     (       A   j     ❘     B   n       )     ⁢     P   ⁡     (     B   n     )         +       P   ⁡     (       A   j     ❘     B   0       )       ⁢     P   ⁡     (     B   0     )                     ⁢     
     ⁢         for   ⁢           ⁢   j     =   0     ,   1     ⁢     
     ⁢       i   =   0     ,   …   ⁢           ,   n             (   2   )             
 
      The expressions in Eq. (2) represent the probability of an explosive category given an alarm (j=1) and given a clear (j=0). Thus, it quantifies the relative certainty of the presence of a bomb when the system  10  output is given.  
      These probabilities depend on quantities of the particular system used (Pd, Pfa) and on the so-called “priors,” which are P(B i ) and  
         P   ⁡     (     B   0     )       =     1   -       ∑     i   =   1     n     ⁢       P   ⁡     (     B   i     )       .             
 
 Prior probabilities are fundamental in Bayesian statistics, and will be discussed in more detail in a later section. The prior probabilities are assigned before screening a bag. 
 
      Conveniently, the computed probabilities (P(B|A), etc.) from one system can act as the prior probabilities (P(B), etc.) for a second system. This is true when the two systems are conditionally independent. An additional assumption is that a bag can only contain one explosive type, i.e., B 1  and B 2  are mutually exclusive. However, the probability of B1 and B2 can both be high, but the sum of B1 and B2 cannot exceed 1, or 100 percent.  
      When the EDS output is generalized from A 1  and A 0  to any output (X), which can be a binary variable (alarm or clear), a set of such variables, a continuous number, a set of continuous numbers, or a mix of all of them, Eq. (3) takes the following form:  
               P   ⁡     (       B   i     ❘   X     )       =         P   ⁡     (     X   ❘     B   i       )       ⁢     P   ⁡     (     B   i     )                     P   ⁡     (     X   ❘     B   1       )       ⁢     P   ⁡     (     B   1     )         +       P   ⁡     (     X   ❘     B   2       )       ⁢     P   ⁡     (     B   2     )         +   …   +                   P   ⁡     (     X   ❘     B   n       )       ⁢     P   ⁡     (     B   n     )         +       P   ⁡     (     X   ❘     B   0       )       ⁢     P   ⁡     (     B   0     )                           (   3   )             
 
      A threat state is defined as the array of probabilities, P(B 1 ),P(B 2 ), . . . ,P(B n ). P(B 0 ) is omitted since it can be computed from the other components, i.e., P(B 0 )=1−(P(B 1 )+P(B 2 )+ . . . +P(B n )). The Bayesian prior forms the initial threat state, i.e., the threat state before the bag has been screened by any EDS. Each EDS modifies the threat state according to its scan results (X), historical data, or likelihood, (P(X|B i )), and the input threat state (P(B i )). Thus, P(B i |X) is the threat state after modification by an EDS.  
      For multiple EDSs operating in series, the output threat state for one EDS is given as the input threat state for the next EDS downstream. The threat state thus propagates through the systems accumulating information from each EDS, as illustrated in  FIGS. 4A-4C .  
      Prior threat assessments, either per threat scenario (threat alert level) or per passenger (Computer Assisted Passenger Prescreening System—“CAPPS”), can be implemented as a meta TSP EDS according to Eq. (4).  
      For each bag, the system makes a binary decision: Alert or Clear. In the TSP protocol this decision is based on the output threat state. It is based on whether the combined probability of contraband, i.e., P(B i |X)+P(B 2 |X)+ . . . +P(B n |X), exceeds a pre-determined threshold, or critical probability (P crit ).  
      There may be particular types of explosives that an EDD is not able to detect. To fill in any possible gaps, the TSP adds a checklist to the threat state. The checklist has one entry per explosive category and is propagated through the system along with the threat state. If one or more entries (types of explosives) are left unchecked, the system will trigger the alarm, no matter what the threat state is. The checklist can be defined as:  
               C   i     =     {         1         if   ⁢           ⁢   screened   ⁢           ⁢   for   ⁢           ⁢     B   i               0       otherwise                   (   4   )             
 
      Thus, the EDS decision can further be defined as:  
               EDS   ⁢           ⁢   decision     =     {               ⁢     Alert   ⁢           ⁢   if                 ∑     i   =   1     n     ⁢     P   ⁡     (     B   i     )         &gt;       P   crit     ⋃       ∑     i   =   1     n     ⁢     C   i         &lt;   n                   ⁢   Clear         otherwise                   (   5   )             
 
      The sensitivity of an EDS may thus be adjusted in two ways: by changing the prior threat state or by changing the critical probability.  
      In use, a container or bag  28  is placed on the conveyor belt  20 . Referring to  FIGS. 3, 4A , and  5 , a personal threat state  32  is first generated (step  30 ). Before the bag  28  is scanned by the CT scanner  16 , information about a person, such as a person taking the bag  28  for loading, is entered into the computer  22  through the alpha-numeric input device  110  and the cursor control device  112 . Depending on the information entered, the instructions  124  are sent to the processor  100  and the main memory  124  and fed into the database  26  as an input  126 . The computer  22  retrieves various information from the database  26  and/or static memory  104 . Based on output information  128  received from the database  26 , the computer  22  generates the personal threat state  32 , which includes probabilities that the person will be carrying one of a number, such as four, types of contraband, such as an explosive device, in their bag  28 . As illustrated in  FIG. 4A , the personal threat state  32  is displayed on the display device  108  of the computer  22 .  
      The bag  28  is then moved along the conveyor belt  20  into the CT scanner  16  (step  34 ). While the bag  28  is within the passageway, the gantry rotates the X-ray source and detector units around the bag  28  so that multiple projections of the bag  28  may be taken at various angles. X-rays emitted from the source pass through the bag and are detected by the detector units. Each image the CT creates represents the mass and density of a two-dimensional “slice” of the bag.  
      As illustrated in  FIG. 4B , the personal threat state  32  is sent to the CT scanner  16  which, after making its observations, modifies the personal threat state  32  to generate an intermediate, or preliminary, threat state  38  (step  36 ). The intermediate threat state  38  includes modified probabilities that the bag  28  includes the various types of contraband that were included in the personal threat state  32 . Because of the various detections made by the CT scanner  16 , the probability for each type of contraband has likely been changed. The intermediate threat state  38  is displayed on the display device  108  of the computer  22 .  
      The conveyor belt  20  then moves the bag  28  into the QR scanner  18 , which scans the bag  28  (step  40 ). As illustrated in  FIG. 4C , the intermediate threat state  38  is sent to the QR scanner which, based on various detections made, modifies the intermediate threat state  38  to generate a final threat state  44  (step  42 ). The final threat state  44  includes a plurality of further modified probabilities that the bag  28  includes one of the various types of contraband included in the intermediate  38  and personal  32  threat states. The final threat state  44  is displayed on the display device  108  of the computer  22 .  
      The computer  22  reads the final threat state  44 , and if the total probability of any type of contraband being in the bag  28  is above the critical probability, the computer  22  triggers the alarm to alert the user of the system  10 , as described in Eq. (6) (step  46 ).  
      One advantage is that because the EDDs communicate through a common protocol, a tailored data fusion algorithm is not required. Another advantage is that intimate knowledge of the individual EDDs and/or EDSs, which may be made by different manufacturers, is not required in order to use the system. A further advantage is that because a prior threat state is incorporated before the bag is scanned with an EDS, a more accurate contraband detection system is provided. A further advantage is that the system categorizes the threat states for different types of explosives. A further advantage is that the sensitivity of the system is easily adjusted by changing the critical probability or by altering the prior threat state through incorporating passenger profiling information or threat alert state information.  
       FIG. 6  illustrates a contraband detection system  50  according to another embodiment of the present invention. The contraband detection system  50  may include components similar to those of the system  10  illustrated in  FIG. 1 . Referring to  FIG. 6 , the contraband detection system  50  includes a database  52 , a first contraband detection apparatus  54 , a second contraband detection apparatus  56 , and a third contraband detection apparatus  58 . In the embodiment illustrated in  FIG. 6 , the first contraband detection apparatus  54  is a CT scanner (hereinafter referred to as “CT scanner  54 ”), the second contraband detection apparatus  56  is a QR scanner (hereinafter referred to as “QR scanner  56 ”), and the third contraband detection apparatus  58  is an x-ray diffraction (XRD) scanner (hereinafter referred to as “XRD scanner  58 ”).  
      Although not illustrated, it should be understood that the contraband detection system  50  may also include a computer similar to the one illustrated in  FIG. 1 .  
      In use, referring to  FIG. 6 , a bag  60  is placed within the system  50 . Before the bag is scanned with the CT scanner  54 , a personal threat state  62  is generated based on information about the carrier of the bag  60  and information retrieved from the database  52  or the computer  22 . When the bag  60  is scanned by the CT scanner  54 , a preliminary threat state  64  is generated, such as by modifying the personal threat state  62 . The bag  60  is then scanned by the QR scanner  56 , and an intermediate threat state  66  is generated, such as by modifying the preliminary threat state  64 . After the bag  60  is scanned by the XRD scanner  58 , a final threat state  68  is generated, such as by modifying the intermediate threat state  66 .  
      The XRD scanner  58 , as is commonly understood in the art, includes an x-ray source and an x-ray detector. X-rays are sent from the x-ray source through the bag  60  into the detector, which measures the elastic or coherent scatter spectra of the x-rays after passing through the bag  60 . The computer may include a library of known reference spectra for various dangerous substances and compare them to the detected spectra.  
      It should be understood that the generating of the various threat states, or modification of the threat states, is performed by the computer, in a similar fashion to the system  10  illustrated in  FIG. 1 .  
      An advantage of the system  50  illustrated in  FIG. 6  is that the accuracy of detecting contraband is even further increased.  
      Other embodiments may use different types of contraband detection apparatuses besides CT, QR, and XRD scanners. For example, an Advanced Technology (AT) hardware scanner, as is commonly understood in the art, may also be used. An AT scanner may include two x-ray systems with two different views of the suspect object (e.g., the bag). The two images created from these views are combined into what is known as a “three-dimensional density reconstruction.” The estimated material density is compared to typical density data for explosive materials. An AT scanner may also include a dual energy explosive detection system to further estimate the density of the objects in the bag. Two different x-ray images are created using two different x-ray voltages. Dedicated image processing is used to separate different objects superimposed on one another in the projected image. The estimated densities are compared to typical density data for explosive materials.  
      Additionally, as another example, a trace detector, as is commonly understood in the art, could also be used. A trace detector essentially “sniffs” an object to determine its composition. A trace detector includes a collector mechanism that traps vapor and particles from the subject object (e.g., the bag). The collected particles are then analyzed to determine the composition of the object.  
      The various types of scanners, or detection apparatuses (e.g., CT, QR, XRD, AT, and trace detectors), can be arranged in the explosive detection system in any order, in any combination (e.g., an XRD, a QR, and a trace detector). More than three detection apparatuses may be linked to use the method described above. The detection apparatuses may be used to detect other types of contraband, such as narcotics. After the personal threat state is generated, the bag may be scanned with only one contraband detection apparatus. The personal threat state may be generated without using information about the particular individual and may simply be a generic personal threat state. The contraband detection apparatuses may not be directly physically or electrically connected, and the scans by each contraband detection apparatuses may not take place immediately after one another.  
      In the cases where the contraband detection apparatus is an imaging system, such as a CT scanner, the system may be able to locate threat items, or regions, within the item being scanned (i.e., the bag). There may be multiple distinct threat regions within the bag. In these cases, the local regions within the bag may each have an associated threat state. The bag will thus have several local threat states and a global threat state. The global threat state is valid for the entire bag and is consistent with the local threat states.  
      There may thus be a hierarchy of threat states consisting of local threat states within global threat states. This hierarchy of threat states may be passed between systems. The calculus for the local threat states is the same as it is for the global threat states. The global threat state may be computed from multiple local threat states by assuming statistical independence between the different threat regions.  
      An additional advantage of this hierarchy of threat states is that the “resolution” of the threat is increased. This resolution is further increased because if the bag is scanned by multiple imaging systems, the second system can modify the local threat states reported by the first system.  
      The TSP protocol is fully defined through Eqs. (4), (5), and (6). However, some guidelines and examples are needed to illustrate how to compute the conditional probabilities, P(X|B i ).  
      Type 1 EDS Single Binary Output  
      If the only information available is whether the EDS is alarmed or not, TSP compliance can be achieved by determining: 
 
P(A|B i ), i∈1, . . . ,n 
 
P(A|{overscore (B)})  (6) 
 
 P ( {overscore (A)}|B   i )=1 −P ( A|B   i ), i∈1, . . . ,n 
 
 P ( {overscore (A)}|{overscore (B)} )=1− P ( A|{overscore (B)})  
 
      The probabilities in Eq. (6) are estimated by using historical performance data for the EDS.  
      To estimate P(A|B i ), detection test data is used. Various samples of explosive type B i  are placed in a sample of bags and run through the EDS. The detection rate that results is taken to represent P(A|B i ).  
      The historical false alarm rate of the EDS is used to estimate P(A|{overscore (B)}).  
      Type 2 EDS, Multiple Alarm Categories, Location Specific (Example CTX)  
      As an extension of the Type 1 EDS, this EDS has: 
          Multiple alarm categories, A 1 , . . . ,A m       Potentially multiple alarms per bag     Each alarm independent, and occurring at separate locations within a bag.        

      An alternative representation of this system is a set of m independent sensors, each of which can output a 0 (Clear), or a discrete number corresponding to the number of alarm items.  
      The output of the EDS is a sequence of A j &#39;s. The number of elements in the sequence is greater than or equal to the number of alarm categories. A couple of examples: 
          A Clear is equivalent to: {overscore (A)} 1 {overscore (A)} 2  . . . {overscore (A)} m , i.e., all sensors clear.     Two alarms in category 2 is equivalent to: {overscore (A)} 1 A 2 A 2 {overscore (A)} 3  . . . {overscore (A)} m .        

      The EDS output, which is written as X in Eq. (3), is replaced by the sequence of A j &#39;s. Because the sensors are independent, and alarms occur at different positions, the conditional probabilities can be written as: 
 
 P ( X|B   i )= P ( {overscore (A)}   1   ,{overscore (A)}   2   , . . . {overscore (A)}   m   |B   i )= P ( {overscore (A)}   1   |B   i )× P ( {overscore (A)}   2   |B   i )× . . . × P ( {overscore (A)}   m   |B   i )  (7) 
 
      What needs to be pre-determined is: 
 
P(A j |B i ) for i∈1, . . . ,n and j∈1, . . . ,m 
 
P(A j |{overscore (B)}) for j∈1, . . . ,m 
 
 P ( {overscore (A)}   j   |B   i )=1− P ( A   j   |{overscore (B)}   i ) for i∈1, . . . ,n and j∈1, . . . ,m  (8) 
 
 P ({overscore (A)} j   |{overscore (B)} )=1 −P ( A   j   |{overscore (B)} ) for j∈1, . . . ,m 
 
      The probabilities in Eq. (8) are estimated by using historical performance data for the EDS.  
      To estimate P(A j |B i ) one needs to use detection test data. Various samples of explosive type B i  are placed in various bags and run through the EDS. The relative frequency of alarm type A j  is taken to represent P(A j |B i ).  
      To estimate P(A j |{overscore (B)}), which is the false alarm rate for alarm type A j , standard system false alarm rates are used.  
      For many EDSs the alarm categories are matched to the explosive categories. For example, the CTX sheet alarm coincides with one of the B i s. In this case, off-diagonal components of P(A j |B i ) can be interpreted as misclassification rates.  
      This method results in a single threat state per bag, even when there were multiple alarms. A single threat state per bag is appropriate to represent the overall threat for the bag, i.e., to make a decision whether to alarm. However, in cases where there are multiple location-sensitive EDSs (e.g., CTX followed by XRD), it is valuable to preserve the localized threat information and propagate also local threat states. The downstream system can then modify the local threat states before combining them to a per-bag threat state.  
      Type 3 EDS/EDD: One or More Features  
      This type of EDS provides one or more real continuous number(s), which are indicative of the presence of explosive in the bag. With a fixed number of features, the EDS is not location-sensitive, i.e., it produces a reading for the bag as a whole. NQR and other non-imaging technologies belong in this category.  
      In the non-TSP case, the EDS decision is usually made by applying one or more thresholds to the features. One way of adapting to TSP is to treat the EDS as a discrete Type 1 (or 2) system, i.e., applying the same threshold(s) and using statistics for false positives, etc. However, much better results are obtained by treating the feature as a continuous distribution. This involves a modeling step.  
      Let&#39;s first adapt the threat state formula in Eq. (3). The output data X is now a fixed-length array of real numbers, X 1 ,X 2 , . . . ,X m . We are assuming that the features are independent, and so we can write: 
 
 P ( X|B   i )= P ( X   1   , X   2   , X   m   |B   i )= P ( X   1   |B   i )× P ( X   2   |B   i )× . . . × P ( X   m   |B   i )  (9) 
 
      For each feature, X j , we now need to determine: 
 
P(X j |B i )  (10) 
 
P(X j |{overscore (B)}) 
 
      For the previous Type 1 or 2 EDSs, these conditional probabilities of Eq. (10) were single numbers (scalars), whereas in this case they are probability distributions for the variable X j .  
      Below is an outline of the steps needed: 
      1. Collect feature data for bags with explosives as well as bags without explosives;     2. Check for correlation between features, if possible de-correlate them (by a linear transformation, Hotelling transform, omission of a feature, etc.);     3. For each feature construct the histograms for threat bags (B i ) and bags without explosives;     4. Fit probability distributions to the histograms. This does not have to be a normal distribution. A transformation of the feature (for example, taking the logarithm of the feature) may, for example, be required to obtain a good fit to a probability distribution.    

      The main advantage of using continuous features is that the exact confidence of an EDS is incorporated into the threat state. When multiple EDSs apply continuous features a “deep” data fusion occurs, where the confidence of the individual systems are weighed in an optimum fashion.  
     EXAMPLE  
     A Single Feature  
      Consider the case of an EDD that produces only a single feature, X 1 , which is indicative of only one explosive type, say B 2 . Then, we need to determine: 
 
P(X 1 ├B 2 ) 
 
 P ( X   1   |B )= P ( X   1   |B   1 )= P ( X   1   |B   3 )= . . . = P ( X   1   |B   n )  (11) 
 
      Note that the second line of Eq. (15) expresses the fact that the feature will have the same distribution for bags with explosive types other than B 2  as for bags with no explosive. A sensor on the EDD cannot detect the whole spectrum of explosives, only B 2 .  
      The modeling of these two probability distributions based on historical data is illustrated in  FIG. 7 . This can be a cumbersome process of trying different probability distribution functions and transforming the feature.  
      Special attention is needed for the extreme values of the feature where one of the probability densities may converge to zero in a too rapid a fashion. A practical and conservative precaution is to define extreme limits beyond which the two probability densities are taken to be identical. This prevents over-confident results for outliers.  
      Following this example through, we can now compute the modified threat state, P(B 2 |X 1 ), which is the probability that a bag has a bomb of category 2 given a measurement of the feature X 1 .  
      Using Eq. (3) and Eq. (11) we get:  
                     P   ⁢     (       B   2     ❘     X   1       )       =       ⁢         P   ⁡     (       X   1     ❘     B   2       )       ⁢     P   ⁡     (     B   2     )                     P   ⁡     (       X   1     ❘     B   2       )       ⁢     P   ⁡     (     B   2     )         +       P   ⁡     (       X   1     ❘     B   _       )       ⁢     (       P   ⁡     (     B   1     )       +                         P   ⁢     (     B   3     )       +   …   +     P   ⁡     (     B   n     )       +     P   ⁡     (     B   _     )         )                         =       ⁢         P   ⁡     (       X   1     ❘     B   2       )       ⁢     P   ⁡     (     B   2     )               P   ⁡     (       X   1     ❘     B   2       )       ⁢     P   ⁡     (     B   2     )         +       P   ⁡     (       X   1     ❘     B   _       )       ⁢     (     1   -     P   ⁡     (     B   2     )         )                         (   12   )             
 
      Using the probability functions from  FIG. 7  and the priors we can compute the function, P(B 2 |X 1 ), as illustrated in  FIG. 8 .  
      This EDD would only be able to check the second item in the checklist, and so, in a stand-alone situation would never be able to clear a bag. Operating in conjunction with an EDS, or another EDD completing the checklist, the EDD would add value with respect to overall EDS performance.  
      Type 4 EDS: Multiple Alarm Categories with Associated Sets of Features (CTX-PDC)  
      This is a hybrid of case 2 and 3, a quite typical case. There can still be multiple alarms, of different categories, in localized positions of a bag. In addition, one or more features are available per alarm item, which allows for a modeling with a probability distribution as was seen for Type 3 EDS. The solution is a thus a combination of Type 2 and 3.  
       FIG. 9  illustrates a distribution of reference quantities, also referred to as “histograms of class data,” measured when scanning reference objects having a first threat type (Threat Type A), a distribution of the reference quantities measured when scanning reference objects having a second threat type (Threat Type B), and a distribution of the reference quantities when scanning reference objects that do not have the first and second threat types (False F). In the present example, a normal distribution is achieved for the objects having the first and second threat types, and for the objects without the first and second threat types. The distributions have difference maximum scores, and the maximum scores are at different values of the reference quantities. The histograms of class data are then converted to modeled Gaussians. The Gaussians are obtained by fitting functions that approximate the distributions, and are thus functions/data representing the distributions. With the Gaussians stored in memory of the computer system, the EDD is now ready for analyzing data in a scanning operation.  
       FIG. 10  illustrates the analysis of a value measured when scanning an inspected object. The value (X New) that is measured is located among the reference quantities on the abscissa of the distributions. In the present case, the value is approximately 22. The score on the distributions corresponding to the value 22 is then determined on the ordinate. In the present example, the score on the distribution representing the first threat type is higher than the score on the distribution representing the second threat type, and both scores representing the first and second threat types are higher than the score representing the absence of the first and second threat types. The representative scores are the class likelihoods corresponding to the value of 22 that is measured.  
      The class likelihoods are multiplied with the priors to obtain the final probability. In the present example, the prior corresponding to False F is more heavily weighted that the probabilities corresponding to the presence of the first and second threat types (Threat Type A and Threat Type B). Once the final probabilities are determined, they are normalized so that they add up to one without modifying their relative weight. In the present example, the normalized probabilities are approximately 0.44, 0.22, and 0.34 corresponding to Threat Type A, Threat Type B, and False F, respectively. The normalized final probabilities indicating the threat types are then summed in order to obtain the combined state. The sum, in the present case, is 0.22 plus 0.44, i.e., 0.66. An alarm would be activated if the final threat state, in the present example 0.66, is more than a predetermined maximum value, for example, 0.4.  
      While certain exemplary embodiments have been described and shown in the accompanying drawings, it is to be understood that such embodiments are merely illustrative and not restrictive of the current invention, and that this invention is not restricted to the specific constructions and arrangements shown and described since modifications may occur to those ordinarily skilled in the art.