Detection of transient signals with unknown localization

In the context of real-time detection of transient signals, a likelihood ratio (LR) test is evaluated at every sampling interval. Performing the LR tests at a lower rate reduces significantly the computational complexity of the detection algorithm. However, in general, this simplification also leads to a strong degradation of the detector performance. For example, with a small shift error, an arriving transient may be in quadrature with its model. This degradation is particularly noticeable when the signals to detect are deterministic and sampled at a frequency close to the Nyquist rate. This letter proposes a method to overcome this limitation by using locally stationary models of the signals to detect. The resulting detectors are robust to shift errors and computationally efficient.