Single - Receiver GPS Phase Bias Resolution

adapt to various targets and datasets. The objective was to design an efficient computer vision system that can learn to detect multiple targets in large images with unknown backgrounds. Because the target size is small relative to the image size in this problem, there are many regions of the image that could potentially contain the target. A cursory analysis of every region can be computationally efficient, but may yield too many false positives. On the other hand, a detailed analysis of every region can yield better results, but may be computationally inefficient. The multi-stage ATR system was designed to achieve an optimal balance between accuracy and computational efficiency by incorporating both models. The detection stage first identifies potential ROIs where the target may be present by performing a fast Fourier domain OT-MACH filter-based correlation. Because threshold for this stage is chosen with the goal of detecting all true positives, a number of false positives are also detected as ROIs. The verification stage then transforms the regions of interest into feature space, and eliminates false positives using an artificial neural network classifier. The multi-stage system allows tuning the detection sensitivity and the identification specificity individually in each stage. It is easier to achieve optimized ATR operation based on its specific goal. The test results show that the system was successful in substantially reducing the false positive rate when tested on a sonar and video image datasets. This work was done by Tien-Hsin Chao, Thomas T. Lu, and David Ye of Caltech; Weston Edens of Butler University; and Oliver Johnson of Harvey Mudd College for NASA’s Jet Propulsion Laboratory. For more information, contact [email protected]. The software used in this innovation is available for commercial licensing. Please contact Daniel Broderick of the California Institute of Technology at [email protected]. Refer to NPO-47012. The Multi-Stage ATR System Architecture incorporates a detection stage that first identifies potential ROIs where the target may be present by performing a Fast Fourier domain OT-MACH filter-based correlation. Training Images