42 research outputs found
Data-Driven Power Control for State Estimation: A Bayesian Inference Approach
We consider sensor transmission power control for state estimation, using a
Bayesian inference approach. A sensor node sends its local state estimate to a
remote estimator over an unreliable wireless communication channel with random
data packet drops. As related to packet dropout rate, transmission power is
chosen by the sensor based on the relative importance of the local state
estimate. The proposed power controller is proved to preserve Gaussianity of
local estimate innovation, which enables us to obtain a closed-form solution of
the expected state estimation error covariance. Comparisons with alternative
non data-driven controllers demonstrate performance improvement using our
approach
Trading off Complexity With Communication Costs in Distributed Adaptive Learning via Krylov Subspaces for Dimensionality Reduction
In this paper, the problemof dimensionality reduction in adaptive distributed learning is studied. We consider a network obeying the ad-hoc topology, in which the nodes sense an amount of data and cooperate with each other, by exchanging information, in order to estimate an unknown, common, parameter vector. The algorithm, to be presented here, follows the set-theoretic estimation rationale; i.e., at each time instant and at each node of the network, a closed convex set is constructed based on the received measurements, and this defines the region in which the solution
is searched for. In this paper, these closed convex sets, known as property sets, take the form of hyperslabs. Moreover, in order to reduce the number of transmitted coefficients, which is dictated by the dimension of the unknown vector, we seek for possible solutions in a subspace of lower dimension; the technique will be developed around the Krylov subspace rationale. Our goal is to find a point that belongs to the intersection of this infinite number of hyperslabs and the respective Krylov subspaces. This is achieved via a sequence of projections onto the property sets and the Krylov subspaces. The case of highly correlated inputs that degrades the performance of the algorithm is also considered. This is overcome via a
transformation whichwhitens the input. The proposed schemes are brought in a decentralized form by adopting the combine-adapt cooperation strategy among the nodes. Full convergence analysis is carried out and numerical tests verify the validity of the proposed schemes in different scenarios in the context of the adaptive distributed system identification task
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
Multi-sensor linear state estimation under high rate quantization
In this paper we consider state estimation of a discrete time linear system using multiple sensors, where the sensors quantize their individual innovations, which are then combined at the fusion center to form a global state estimate. We obtain an asymptotic approximation for the error covariance matrix that relates the system parameters and quantization levels used by the different sensors. Numerical results show close agreement with the true error covariance for quantization at high rates. An optimal rate allocation problem amongst the different sensors is also considered
A Nonstochastic Information Theory for Communication and State Estimation
In communications, unknown variables are usually modelled as random
variables, and concepts such as independence, entropy and information are
defined in terms of the underlying probability distributions. In contrast,
control theory often treats uncertainties and disturbances as bounded unknowns
having no statistical structure. The area of networked control combines both
fields, raising the question of whether it is possible to construct meaningful
analogues of stochastic concepts such as independence, Markovness, entropy and
information without assuming a probability space. This paper introduces a
framework for doing so, leading to the construction of a maximin information
functional for nonstochastic variables. It is shown that the largest maximin
information rate through a memoryless, error-prone channel in this framework
coincides with the block-coding zero-error capacity of the channel. Maximin
information is then used to derive tight conditions for uniformly estimating
the state of a linear time-invariant system over such a channel, paralleling
recent results of Matveev and Savkin