DECENTRALIZED SEQUENTIAL DETECTION WITH SENSORS PERFORMING SEQUENTIAL TESTS
Venugopal V. Veeravalli
Tamer Basar
H. Vincent Poor
Abstract
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 (GSPRT's) 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 SPRT's at each of the sensors.