Multi-model combined SPRT for detection with uncertain hypothesis distribution

The Sequential Probability Ratio Test (SPRT) is a classical detector for problems with an unfixed sample size. Though it is optimal under some conditions, SPRT can be directly used only for a binary hypothesis with exactly known distributions. In this paper, sequential detection problem with an uncertain hypothesis distribution is considered, in which the uncertain distribution is formulated in a multi-model form. A combined SPRT algorithm is given based on the multi-model set. The detection performance and model design of the algorithm are analyzed, especially for the Gaussian distribution problem. Simulation results show that the proposed algorithm can handle the uncertain detection problem effectively.