Achieving Cramer–Rao Lower Bounds in Sensor Network Estimation

Achievable bounds for parametric estimation using sensor networks are known for some specific problems. In this article, achievable bounds for the general parametric estimation problem with fixed and varying parameters in the network and for various noise conditions on sensor nodes are studied. The general Cramer–Rao lower bounds (CRLBs) of parameter estimates using sensor array network are derived. Procedures for reaching these bounds can be designed based on the obtained results. The accuracy of derived relationships is confirmed by an example. It is shown that it is possible to develop algorithms of moderate complexity and communication cost achieving the CRLB.