Adaptive detection based on the Krylov subspace techniques

A new type of adaptive detectors based on the Krylov subspace techniques are proposed; and their detection performance are improved compared to conventional detectors, especially when the number of the training data is small. More importantly, the new detectors are robust of the rank selection. Accounting for the rounding error in real-world applications, numerically stable detectors are also presented.