Decentralized sequential detection with sensors performing sequential tests

A decentralized sequential detection problem is considered where a set of sensors making independent observations must decide which of the given two hypotheses is true. Decision errors are penalized through a common cost function, and each time step taken by the sensors as a team is assigned a positive cost. It is shown that optimal sensor decision functions can be found in the class of generalized sequential probability ratio tests (GSPRTs) with monotonically convergent thresholds. A technique is presented for obtaining the optimal thresholds. The performance of the optimal policy is compared with that of a policy which uses SPRTs at each of the sensors.