34 research outputs found
Theoretical Bounds in Minimax Decentralized Hypothesis Testing
Minimax decentralized detection is studied under two scenarios: with and
without a fusion center when the source of uncertainty is the Bayesian prior.
When there is no fusion center, the constraints in the network design are
determined. Both for a single decision maker and multiple decision makers, the
maximum loss in detection performance due to minimax decision making is
obtained. In the presence of a fusion center, the maximum loss of detection
performance between with- and without fusion center networks is derived
assuming that both networks are minimax robust. The results are finally
generalized.Comment: Submitted to IEEE Trans. on Signal Processin
On the genericity properties in networked estimation: Topology design and sensor placement
In this paper, we consider networked estimation of linear, discrete-time
dynamical systems monitored by a network of agents. In order to minimize the
power requirement at the (possibly, battery-operated) agents, we require that
the agents can exchange information with their neighbors only \emph{once per
dynamical system time-step}; in contrast to consensus-based estimation where
the agents exchange information until they reach a consensus. It can be
verified that with this restriction on information exchange, measurement fusion
alone results in an unbounded estimation error at every such agent that does
not have an observable set of measurements in its neighborhood. To over come
this challenge, state-estimate fusion has been proposed to recover the system
observability. However, we show that adding state-estimate fusion may not
recover observability when the system matrix is structured-rank (-rank)
deficient.
In this context, we characterize the state-estimate fusion and measurement
fusion under both full -rank and -rank deficient system matrices.Comment: submitted for IEEE journal publicatio