KEYWORDS: Target detection, Automatic target recognition, Data modeling, Systems modeling, Statistical analysis, Monte Carlo methods, Target recognition, Palladium, Error analysis, Detection and tracking algorithms
In Automatic Target Recognition (ATR) systems Receiver Operating Characteristics (ROC) curves are used to describe operating characteristics for changing threshold values of two aspects of importance to the users. the two aspects are the ability of the Automatic Target Recognition (ATR) system to detect targets and its ability to reject non-targets that is, not declare false alarms. These two abilities are represented in ATR systems analysis by the probability of detection and the false alarm rate. The problem of characterizing the confidence area for parameters of the probability distribution of ROC points is addressed and applied to the Moving and Stationary Target Acquisition and Recognition (MSTAR) DEMO III data using MATLAB.
Synthetic Aperture Radar (SAR) image scene modeling tools are of high interest to Automatic Target Recognition (ATR) algorithm evaluation because they allow the testing of ATR's over a wider range of extended operating conditions (EOCs). Typical EOCs include target aspect, target configuration, target obscuration, and background terrain variations. A baseline terrain image synthesis technique empirically derived probability density functions (pdfs) for various terrain types from measured data to allow the simulation of user defined scenes. Initial full scene simulation experiments that applied this technique to the MSTAR data showed that using measured images as a data source for creating distribution functions in artificial scenes can introduce error, unless the proper spatial autocorrelation is also modeled. Measured SAR scene pixels have non-zero autocorrelation that blurs edges between different terrain types and creates a texture in the clutter regions of the image. Unfortunately, applying simple blurring techniques, such as a moving weighted window, to model autocorrelation mutes the second and higher moments of the pixel amplitude statistics. We propose a technique that models spatial autocorrelation while preserving the desired the amplitude statistics within each defined terrain class group.
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