20 research outputs found
Kalman Filtering With Relays Over Wireless Fading Channels
This note studies the use of relays to improve the performance of Kalman
filtering over packet dropping links. Packet reception probabilities are
governed by time-varying fading channel gains, and the sensor and relay
transmit powers. We consider situations with multiple sensors and relays, where
each relay can either forward one of the sensors' measurements to the
gateway/fusion center, or perform a simple linear network coding operation on
some of the sensor measurements. Using an expected error covariance performance
measure, we consider optimal and suboptimal methods for finding the best relay
configuration, and power control problems for optimizing the Kalman filter
performance. Our methods show that significant performance gains can be
obtained through the use of relays, network coding and power control, with at
least 30-40 less power consumption for a given expected error covariance
specification.Comment: 7 page
Adaptive Controller Placement for Wireless Sensor-Actuator Networks with Erasure Channels
Wireless sensor-actuator networks offer flexibility for control design. One
novel element which may arise in networks with multiple nodes is that the role
of some nodes does not need to be fixed. In particular, there is no need to
pre-allocate which nodes assume controller functions and which ones merely
relay data. We present a flexible architecture for networked control using
multiple nodes connected in series over analog erasure channels without
acknowledgments. The control architecture proposed adapts to changes in network
conditions, by allowing the role played by individual nodes to depend upon
transmission outcomes. We adopt stochastic models for transmission outcomes and
characterize the distribution of controller location and the covariance of
system states. Simulation results illustrate that the proposed architecture has
the potential to give better performance than limiting control calculations to
be carried out at a fixed node.Comment: 10 pages, 8 figures, to be published in Automatic
Remote State Estimation with Smart Sensors over Markov Fading Channels
We consider a fundamental remote state estimation problem of discrete-time
linear time-invariant (LTI) systems. A smart sensor forwards its local state
estimate to a remote estimator over a time-correlated -state Markov fading
channel, where the packet drop probability is time-varying and depends on the
current fading channel state. We establish a necessary and sufficient condition
for mean-square stability of the remote estimation error covariance as
, where denotes the
spectral radius, is the state transition matrix of the LTI system,
is a diagonal matrix containing the packet drop probabilities in
different channel states, and is the transition probability matrix
of the Markov channel states. To derive this result, we propose a novel
estimation-cycle based approach, and provide new element-wise bounds of matrix
powers. The stability condition is verified by numerical results, and is shown
more effective than existing sufficient conditions in the literature. We
observe that the stability region in terms of the packet drop probabilities in
different channel states can either be convex or concave depending on the
transition probability matrix . Our numerical results suggest that
the stability conditions for remote estimation may coincide for setups with a
smart sensor and with a conventional one (which sends raw measurements to the
remote estimator), though the smart sensor setup achieves a better estimation
performance.Comment: The paper has been accepted by IEEE Transactions on Automatic
Control. Copyright may be transferred without notice, after which this
version may no longer be accessibl
Stabilizing Stochastic Predictive Control under Bernoulli Dropouts
This article presents tractable and recursively feasible optimization-based
controllers for stochastic linear systems with bounded controls. The stochastic
noise in the plant is assumed to be additive, zero mean and fourth moment
bounded, and the control values transmitted over an erasure channel. Three
different transmission protocols are proposed having different requirements on
the storage and computational facilities available at the actuator. We optimize
a suitable stochastic cost function accounting for the effects of both the
stochastic noise and the packet dropouts over affine saturated disturbance
feedback policies. The proposed controllers ensure mean square boundedness of
the states in closed-loop for all positive values of control bounds and any
non-zero probability of successful transmission over a noisy control channel
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