Asymptotically optimal ranking algorithms for quasi-determinate signal detection

The structure of an asymptotically optimal ranking algorithm for quasideterminate signal detection in which the signal has a constant component, the distribution density of the interference of a known type, and the constant component and intensity of fluctuations of the interference unknown are synthesized. Conditions for optimality are defined and analytical expressions for the detection characteristic are obtained. The asymptotic effectiveness of ranking algorithms for cases in which the actual received signal and interference density differ from the expected models was established. The findings relate to quasideterminate signals which are represented as expansions with respect to an orthonormal system of functions with random parameters uniformly distributed on a hypersphere.