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An interactive approach to constructing small LR(1) grammars is presented. An example involving parsing permutations is used to illustrate the approach.

Target detection is limited based on a specific sensors capability; however, the combination of multiple sensors will improve the confidence of target detection. Confidence of detection, tracking and identifying a target in a multi-sensor environment depends on intrinsic and extrinsic sensor qualities, e.g. target geo-location registration, and environmental conditions 1 . Determination of the optimal sensors and classification algorithms, required to assist in specific target detection, has largely been accomplished with empirical experimentation. Formulation of a multi-sensor effectiveness metric (MuSEM) for sensor combinations is presented in this paper. Leveraging one or a combination of sensors should provide a higher confidence of target classification. This metric incorporates the Dempster-Shafer Theory for decision analysis. MuSEM is defined for weakly labeled multimodal data and is modeled and trained with empirical fused sensor detections; this metric is compared to Boolean algebra algorithms from decision fusion research. Multiple sensor specific classifiers are compared and fused to characterize sensor detection models and the likelihood functions of the models. For area under the curve (AUC), MuSEM attained values as high as .97 with an average difference of 5.33% between Boolean fusion rules. Data was collected from the Air Force Research Lab's Minor Area Motion Imagery (MAMI) project. This metric is efficient and effective, providing a confidence of target classification based on sensor combinations.

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