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 thus helpful in many instances to define a set of "features" with different amounts of noise resistance that still provide helpful and adequate spatial information. A "feature" may thus be any combination of detector signals in the detector array. Some of the features may be simple; others may be complicated (such as the reconstructed three-dimensional nitrogen density distribution), and may require imaging techniques. A set of features can be defined by: (1) calculating or estimating certain anticipated features that would normally be present for a set of typical objects (e.g., pieces of luggage) containing explosives; (2) developing calibration techniques using different sets of these features; and (3) testing the performance of the calibration techniques on a test set of data.
Experiments have shown that the variance for any feature is roughly the same for objects with explosives as it is for objects without explosives, even though, of course, the values of the feature may differ between the two classes of objects. It is also true that the distribution of "bombness" (i.e., the likelihood of explosive materials being present) in objects, such as suitcases, is decidedly non-normal. Most suitcases, for example, contain little nitrogen, and are thus quite easy to classify as non-threatening (containing no explosives). However, a significant percentage of the pieces of luggage encountered in airports and similar locations contain sufficient nitrogen and other interfering materials to falsely alarm the detection system, absent some further classification criteria. The distribution of "bombness" thus follows (roughly) the distribution of nitrogen, that is, with most pieces of luggage occurring at low levels of threat but with an extensive tail to the distribution. The addition of a simulated threat shifts the distribution without distorting its shape. This results in the variance for any feature being the same for objects with and without threats, as stated above.
The first (and more conventional) approach used to cla