111 research outputs found
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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The Nehari Shuffle: FIR(q) Filter Design With Guaranteed Error Bounds
This paper presents a new approach to the problem of designing a finite impulse response filter of specified length, q, which approximates in uniform frequency (L-infinity) norm a given desired (possibly infinite impulse responmse) causal, stable filter transfer function. We derive an algorithm-independent lower bound on the achievable approximation error and then present and approximation method which involves the solution of a fixed number of all-pass (Nehari) extension problems and so is called the Nehari shuffle. Upper and lower bounds on the approximation error are derived for the algorithm. These bounds are calculable a priori so the length of the filter can be found before designing the filter. Examples indicate that the method closely approaches the derived global lower bound. We compare the new method with the Preuss (complex Remez exchange) algorithm in some examples
FIR(q) Filter Design Without the Linear Phase Contraint
This paper presents a new approach to the problem of designing a finite impulse response filter of specified length, q, which approximates in uniform frequency norm a given desired (possibly infinite impulse response) filter transfer function
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