Communication Efficient Signal Detection in Correlated Clutter for Wireless Sensor Networks
The authors study a problem of detecting deterministic signals buried in correlated clutter using wireless sensor networks. They are specifically interested in developing a distributed algorithm over the network to detect the presence of a deterministic signal while keeping low communication delay and energy associated with the distributed computation. In this paper, they deploy a distributed version of the Sparse Matrix Transform (SMT) that decorrelates a signal measured by a number of sensors in order to compute a matched filter. The matched filter represents the sum of the log-likelihood ratios over all the sensors of the two hypotheses corresponding to whether a deterministic signal is present or not.