48,140 research outputs found
Optimal Estimation with Limited Measurements and Noisy Communication
This paper considers a sequential estimation and sensor scheduling problem
with one sensor and one estimator. The sensor makes sequential observations
about the state of an underlying memoryless stochastic process, and makes a
decision as to whether or not to send this measurement to the estimator. The
sensor and the estimator have the common objective of minimizing expected
distortion in the estimation of the state of the process, over a finite time
horizon, with the constraint that the sensor can transmit its observation only
a limited number of times. As opposed to the prior work where communication
between the sensor and the estimator was assumed to be perfect (noiseless), in
this work an additive noise channel with fixed power constraint is considered;
hence, the sensor has to encode its message before transmission. For some
specific source and channel noise densities, we obtain the optimal encoding and
estimation policies in conjunction with the optimal transmission schedule. The
impact of the presence of a noisy channel is analyzed numerically based on
dynamic programming. This analysis yields some rather surprising results such
as a phase-transition phenomenon in the number of used transmission
opportunities, which was not encountered in the noiseless communication
setting.Comment: X. Gao, E. Akyol, and T. Basar. Optimal estimation with limited
measurements and noisy communication. In 54th IEEE Conference on Decision and
Control (CDC15), 2015, to appea
Transmission Power Scheduling for Energy Harvesting Sensor in Remote State Estimation
We study remote estimation in a wireless sensor network. Instead of using a
conventional battery-powered sensor, a sensor equipped with an energy harvester
which can obtain energy from the external environment is utilized. We formulate
this problem into an infinite time-horizon Markov decision process and provide
the optimal sensor transmission power control strategy. In addition, a
sub-optimal strategy which is easier to implement and requires less computation
is presented. A numerical example is provided to illustrate the implementation
of the sub-optimal policy and evaluation of its estimation performance.Comment: Extended version of article to be published in the Proceedings of the
19th IFAC World Congress, 201
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
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