A new criterion for optimal constrained minimax detection and classification

This paper addresses the problem of anomaly detection and classification by using a noisy measurement vector corrupted by some linear unknown nuisance parameters. An invariant constrained asymptotically uniformly minimax test is proposed. It minimizes the maximum false classification probability as the signal-to-noise ratio becomes arbitrary large, uniformly with respect to the unknown anomaly amplitude and independently on the nuisance parameters. The probability of maximum classification error is calculated in a closed-form.