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We present a data-driven approach for target detection and identification based on a linear mixture model. Our aim is to determine the existence of certain targets in a mixture without specific information on the targets or the background, and to identify the targets from a given library. We use the maximum canonical correlation between the target set and the observations as the detection score, and...
We present an unsupervised detection approach, detection with canonical correlation (DCC), for target detection based on a linear mixture model. Our aim is determining the existence of certain targets in a given mixture without specific information on the targets or the background. We use canonical correlations between the target set and the mixed components as the detection index, such that the coefficients...
A detection approach, detection with correlation bound (DCB), is introduced based on a linear mixture model. We use the upper bound of the correlation between the target and mixing components as the detection index, and derive the expression for this correlation bound using the observed data. The proposed method is more robust and provides better detection performance than the currently used supervised...
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