Artificial neural networks are applied to the automated classification of trichloroethylene (TCE) signatures from passive Fourier transform infrared remote sensing interferogram data. Through the use of three data collection methods, a combination of laboratory and field data is acquired that allows the methodology to be evaluated under a variety of infrared background conditions and in the presence of potentially interfering compounds such as sulfur hexafluoride, methyl ethyl ketone, acetone, carbon tetrachloride, and ammonia. To maximize the computational efficiency of the network optimization, experimental design techniques are employed to develop a training protocol for the network that takes into account the relationships among five variables that are related either to the network architecture or to the training process. This protocol is implemented for the case of a back-propagation neural network (BNN) and is used to develop an optimized network for the detection of TCE. The classification performance of the network is assessed by comparing both TCE detection capabilities and false detection rates to similar classification results obtained with the technique of piecewise linear discriminant analysis (PLDA). When applied to prediction data withheld from the optimization of both the BNN and PLDA algorithms, the BNN method is observed to outperform PLDA overall, with TCE detection rates in excess of 99% and false detection rates less than 0.5%.

Recent advances in wide-area differential GPS and dual-frequency receiver technology have made dramatic accuracy improvements feasible for Precise Positioning Service (PPS) users. Through a set of distinct upgrades to the GPS control segment, signal-in-space (SIS) accurac,y can be reduced to well under 1 m. Combined with optional user equipment upgrades, SIS error reduction makes submeter positioning accuracy achievable without the additional data link and reference receiver(s) generally required for differential systems.This paper focuses on the effectiveness of several SIS accuracy enhancement concepts. Results of parametric satellite monitoring experiments are used to divide control segment enhancements into subcategories based upon satellite monitoring accuracy and drift error characteristics.This parametric approach provides a framework for further comparisons of the various complementary and competing concepts under consideration. User-related errors such as multipath and atmospheric delays are also discussed to place SIS accuracy in the context of the overall PPS system.

The submitted manuscript has Ix_en authored by a contractor of the U.S. Government ul_rfer conlract No. W-31.109-ENG-38. I Acc()rdingly, the U. S. Government retains a / nof_exclusive, royalty-free license to publish I('r reproduce the published form of this conzribution, or allow others to do so, for LU_. S.___._. Government purposes. DISCLAIMER This report was preparedas an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views _4__ and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.

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