f gamma rays at specified energy levels detected by each detector in the array provides a measure of the particular element, or combination of elements, present within the irradiated slice or section of the object characteristic of that level of gamma ray emission. By combining this information from all of the detectors, as well as by considering the number of neutrons that pass through the object without interacting with atomic nuclei (as sensed by the neutron detector 146), a more accurate density distribution of the elements within the irradiated section of the object may be obtained. By combining such density information for all of the slices of the object that are created as the object moves past the neutrons 132, a three-dimensional density image of the contents of the object can thus be formulated.
Advantageously, the nuclear density information obtained using the gamma ray and neutron detectors as above described can be further enhanced by combining it with the electron density information obtained from the X-ray system 150. If such combined density information suggests the presence of contraband, the object is flagged for further investigation (e.g., diverted off of the conveyor belt for a manual search).
Referring back to the embodiment of the invention in FIG. 4, it is seen that the outputs of the A/D converters 56 are directed to a computer 58. It is one of the main functions of the computer 56 to analyze the data (e.g., the number of gamma rays at a particular energy) sensed by each of the detectors 48 in order to accurately and rapidly classify an object as one with or without explosives. A similar function must be performed by the computer 154 shown in the embodiment of FIG. 7. (Other functions, such as record keeping and miscellaneous housekeeping functions associated with the explosive detection system may also be performed by the computers 58 or 154, but such functions are viewed as conventional functions and will not be described herein.)
There are a number of data analysis techniques that can be used within the computers to achieve their classification function. Two such approaches are described herein: (1) standard decision analysis; and (2) artificial neural system (ANS) analysis. Of these two approaches, the preferred approach is ANS because, as indicated below, it provides a PD that is as much as 2.5% greater than the PD achieved using standard decision analysis at the same throughput and PFA levels.
Regardless of which approach is used, however, it is helpful in some instances (but not all instances) to first identify certain "features" that are present in the detector and other data. By identifying such features within the data, and presenting such features to the ANS system (as opposed to presenting raw detector data to the ANS system) the classification function can be significantly simplified, even though the set-up and calibration of the ANS system (i.e., the process of determining the features) may be somewhat more complicated.
Each individual detector in the system produces some information about the amount and location of the elements, e.g., nitrogen, in the object under examination. Unfortunately, due to interference with other elements, statistical fluctuations, and the like, there is a substantial amount of noise present in the system. In fact, the signal-to-noise ratio for any single detector is rather low. This low signal-to-noise ratio can be improved somewhat by combining the information from several detectors with overlapping fields of view. For example, by averaging two detectors, the signal-to-noise ratio can be improved by a factor of the square root of two. Generally, however, combining more and more detectors worsens the spatial resolution of the system. Hence, a tradeoff must be sought that provides an acceptable signal-to-noise ratio on the one hand and an acceptable spatial resolution on the other hand. Both criteria are required to improve the ability of the system to detect explosives.
To satisfy both the spatial resolution and signal-to-noise ratio requirements, it is t