426 research outputs found
Sequential Detection with Mutual Information Stopping Cost
This paper formulates and solves a sequential detection problem that involves
the mutual information (stochastic observability) of a Gaussian process
observed in noise with missing measurements. The main result is that the
optimal decision is characterized by a monotone policy on the partially ordered
set of positive definite covariance matrices. This monotone structure implies
that numerically efficient algorithms can be designed to estimate and implement
monotone parametrized decision policies.The sequential detection problem is
motivated by applications in radar scheduling where the aim is to maintain the
mutual information of all targets within a specified bound. We illustrate the
problem formulation and performance of monotone parametrized policies via
numerical examples in fly-by and persistent-surveillance applications involving
a GMTI (Ground Moving Target Indicator) radar
Stable state and signal estimation in a network context
Power grid, communications, computer and product reticulation networks are
frequently layered or subdivided by design. The layering divides
responsibilities and can be driven by operational, commercial, regulatory and
privacy concerns. From a control context, a layer, or part of a layer, in a
network isolates the authority to manage, i.e. control, a dynamic system with
connections into unknown parts of the network. The topology of these
connections is fully prescribed but the interconnecting signals, currents in
the case of power grids and bandwidths in communications, are largely
unavailable, through lack of sensing and even prohibition. Accordingly, one is
driven to simultaneous input and state estimation methods. We study a class of
algorithms for this joint task, which has the unfortunate issue of inverting a
subsystem, which if it has unstable transmission zeros leads to an unstable and
unimplementable estimator. Two modifications to the algorithm to ameliorate
this problem were recently proposed involving replacing the troublesome
subsystem with its outer factor from its inner-outer factorization or using a
high-variance white signal model for the unknown inputs. Here, we establish the
connections between the original estimation problem for state and input signal
and the estimates from the algorithm applied solely to the outer factor. It is
demonstrated that the state of the outer factor and that of the original system
asymptotically coincide and that the estimate of the input signal to the outer
factor has asymptotically stationary second-order statistics which are in
one-to-one correspondence with those of the input signal to the original
system, when this signal is itself stationary. Thus, the simultaneous input and
state estimation algorithm applied just to the outer factor yields an unbiased
state estimate for control and the statistics of the interface signals.Comment: 12 pages, 1 figur
The use of fake algebraic Riccati equations for co-channel demodulation
Copyright © 2003 IEEEThis paper describes a method for nonlinear filtering based on an adaptive observer, which guarantees the local stability of the linearized error system. A fake algebraic Riccati equation is employed in the calculation of the filter gain. The design procedure attempts to produce a stable filter at the expense of optimality. This contrasts with the extended Kalman filter (EKF), which attempts to preserve optimality via its linearization procedure, at the expense of stability. A passivity approach is applied to deduce stability conditions for the filter error system. The performance is compared with an EKF for a co-channel frequency demodulation application.Einicke, G.A.; White, L.B.; Bitmead, R.R
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Preserving Linear Design Capabilities in the Nonlinear Control of Nonholonomic Autonomous Underwater Vehicles
We derive here an approach to the nonlinear control of a particular autonomous underwater vehicle architecture. This approach is based on state-variable feedback and estimation in the nonlinear setting but uses many techniques from Linear Quadratic Gaussian methods which are capable of preserving the design aspects of the formulation. The specific task that we consider is the tracking of an unknown ocean floor using current altitude measurements. By guarding the linear aspects as long as possible, we are able to formulate this problem as one of classical disturbance rejection in which {\em a priori} information about the ocean floor may be easily included. The migration from linear to nonlinear control is then performed so as to preserve as many linear design features as is possibl
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