Adaptive Detection of Rician Targets

We address the problem of detecting a signal of interest in Gaussian noise with an unknown covariance matrix, when the amplitude of the signal fluctuates along the observations and follows a Rice distribution. This is typical of a target that consists of one large dominant scatterer and a collection of small independent scatterers. We formulate it as a composite hypothesis testing problem, for which we derive the generalized likelihood ratio test, and show that it ensures a constant false alarm rate. Numerical simulations enable to assess its performance for Rician as well as Swerling I and III targets. It is shown that the new detector incurs no loss for Swerling targets but can offer a significant improvement for Rician targets, especially when the number of training samples is small.