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We provide a general framework for computing the state density of a noisy system given the sequence of hitting times of predefined thresholds. Our method relies on eigenfunction expansion corresponding to the Fokker–Planck operator of the diffusion process. For illustration, we present a particular example in which the state and the noise are one-dimensional Gaussian processes and observations are...
We study the problem of link scheduling for discrete-time agents to achieve average consensus in finite time under communication constraints. We provide necessary and sufficient conditions under which finite time consensus is possible. Furthermore, we prove bounds on the consensus time and exhibit provably optimal communication policies. We also discuss the dual problem of designing communication...
We consider the problem of state estimation of a discrete time process over a packet dropping network. Previous pioneering work on Kalman filtering with intermittent observations is concerned with the asymptotic behavior of E[Pk], i.e., the expected value of the error covariance, for a given packet arrival rate. We consider a different performance metric, Pr[Pk les M], i.e., the probability that P...
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