Nicholas Asendorf

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In this talk, I will analyze the performance of a matched subspace detector (MSD) where the test signal vector is assumed to reside in an unknown, low-rank k subspace that must be estimated from finite, noisy, signal-bearing training data. Under both a stochastic and deterministic model for the test vector, subspace estimation errors due to limited training(More)
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