5 July 1995 Discrimination requirements model
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The development of a discrimination software testbed, the Discrimination Requirements Model (DRM), intended to support IR sensor requirements definition is described. The DRM employs the standard pattern recognition paradigm, e.g., the reduction of a Monte Carlo database of noised input object signatures into the corresponding target and non-target feature vector sets. Classification of the feature vectors is performed using a varied threshold. The false alarm (PFA) and leakage (PL) error probabilities are estimated via a leave-one- out procedure. The resultant PFA versus PL curve of user selectable thresholds is used to evaluate discrimination performance for the test signature database. Degradation of input signature data strings is accomplished through a set of user selectable sensor performance capabilities. The selectable feature subset includes statistical, curvilinear fit, dynamical, and centralized moment-based parameters for single and multiple band optical systems as well as various normalization options. The DRM accommodates dropouts and other realistic SNR effects. A centralized approach is employed for multiple sensor data fusion for discrimination based on prior associated object tracks. Applications to sensor design and system performance projections are discussed.
© (1995) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Robert F. Cuffel, Lisa A. Strugala, C. Pham, James A. Kiessling, P. W. Kelsey, "Discrimination requirements model", Proc. SPIE 2484, Signal Processing, Sensor Fusion, and Target Recognition IV, (5 July 1995); doi: 10.1117/12.213033; https://doi.org/10.1117/12.213033



Monte Carlo methods

Error analysis



Statistical analysis


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