A new algorithm for distributed nonparametric sequential detection

We consider non parametric sequential hypothesis testing problem when the distribution under the null hypothesis is fully known but the alternate hypothesis corresponds to some other unknown distribution with some loose constraints. We propose a simple algorithm to address the problem. This is also generalized to the case when the distribution under the null hypothesis is not fully known. These problems are primarily motivated from wireless sensor networks and spectrum sensing in Cognitive Radios. A decentralized version utilizing spatial diversity is also proposed. Its performance is analysed and asymptotic properties are proved. The simulated and analysed performance of the algorithm are shown to be better than an earlier algorithm addressing the same problem with similar assumptions. We also modify the algorithm for optimizing performance when information about the prior probabilities of occurrence of the two hypotheses are known.