13,655 research outputs found
Factor Analysis of Moving Average Processes
The paper considers an extension of factor analysis to moving average
processes. The problem is formulated as a rank minimization of a suitable
spectral density. It is shown that it can be adequately approximated via a
trace norm convex relaxation
Optimal Energy Allocation for Kalman Filtering over Packet Dropping Links with Imperfect Acknowledgments and Energy Harvesting Constraints
This paper presents a design methodology for optimal transmission energy
allocation at a sensor equipped with energy harvesting technology for remote
state estimation of linear stochastic dynamical systems. In this framework, the
sensor measurements as noisy versions of the system states are sent to the
receiver over a packet dropping communication channel. The packet dropout
probabilities of the channel depend on both the sensor's transmission energies
and time varying wireless fading channel gains. The sensor has access to an
energy harvesting source which is an everlasting but unreliable energy source
compared to conventional batteries with fixed energy storages. The receiver
performs optimal state estimation with random packet dropouts to minimize the
estimation error covariances based on received measurements. The receiver also
sends packet receipt acknowledgments to the sensor via an erroneous feedback
communication channel which is itself packet dropping.
The objective is to design optimal transmission energy allocation at the
energy harvesting sensor to minimize either a finite-time horizon sum or a long
term average (infinite-time horizon) of the trace of the expected estimation
error covariance of the receiver's Kalman filter. These problems are formulated
as Markov decision processes with imperfect state information. The optimal
transmission energy allocation policies are obtained by the use of dynamic
programming techniques. Using the concept of submodularity, the structure of
the optimal transmission energy policies are studied. Suboptimal solutions are
also discussed which are far less computationally intensive than optimal
solutions. Numerical simulation results are presented illustrating the
performance of the energy allocation algorithms.Comment: Submitted to IEEE Transactions on Automatic Control. arXiv admin
note: text overlap with arXiv:1402.663
On the well-posedness of multivariate spectrum approximation and convergence of high-resolution spectral estimators
In this paper, we establish the well-posedness of the generalized moment
problems recently studied by Byrnes-Georgiou-Lindquist and coworkers, and by
Ferrante-Pavon-Ramponi. We then apply these continuity results to prove almost
sure convergence of a sequence of high-resolution spectral estimators indexed
by the sample size
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