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In this paper, it is shown that the problem of communication-limited stabilization is related to the concept of topological entropy, introduced by Adler et. al. as a measure of the information rate of a continuous map on a compact topological space. Using open covers, the notion of topological feedback entropy (TFE) is defined and proposed as a measure of the inherent rate at which a map on a noncompact topological space with inputs generates stability information. It is then proven that a topological dynamical plant can be stabilized into a compact set if and only if the data rate in the feedback loop exceeds the TFE of the plant on the set.

This paper considers the problem of forming and maintaining tracks when measurements have both uncertain origin and are corrupted by additive sensor noise. The spatial clutter measurement density is assumed nonhomogeneous and known. The PPDA-MAP algorithm provides a set of recursive formulae for data association and probability of target existence, thus enabling automatic track initiation, track maintenance, and track termination. New values for initial probability of target existence for IPDA-type algorithm are also derived. Simulation results compare the performance of IPDA-MAP with IPDA, IMM-PDA, IMM-PDA-MAP, EB-PDA and EB-PDA-MAP in a heavy and nonuniform clutter situation.

Feedback control with limited data rates is an emerging area which incorporates ideas from both control and information theory. A fundamental question it poses is how low the closed-loop data rate can be made before a given dynamical system is impossible to stabilize by any coding and control law. Analogously to source coding, this defines the smallest error-free data rate sufficient to achieve "reliable" control, and explicit expressions for it have been derived for linear time-invariant systems without disturbances. In this paper, the more general case of finite-dimensional linear systems with process and observation noise is considered, the object being mean square state stability. By inductive arguments employing the entropy power inequality of information theory, and a new quantizer error bound, an explicit expression for the infimum stabilizing data rate is derived, under very mild conditions on the initial state and noise probability distributions.

The variable structure multiple model (VSMM) approach to the maneuvering target tracking problem is considered. A new VSMM design, the minimal submodel-set switching (MSMSS) algorithm for tracking a maneuvering target is presented. The MSMSS algorithm adaptively determines the minimal set of models from the total model set and uses this to perform multiple models (MM) estimation. In addition, an iterative MSMSS algorithm with improved maneuver detection and termination properties is developed. Simulations results demonstrate that, compared with a standard interacting MM (IMM), the proposed algorithms require significantly lower computation while maintaining similar tracking performance. Alternatively, for a computational load similar to IMM, the new algorithms display significantly improved performance.

Target tracking using delayed, out-of-sequence measurements is a problem of growing importance due to an increased reliance on networked sensors interconnected via complex communication network architectures. In such systems, it is often the case that measurements are received out-of-time-order at the fusion center. This paper presents a Bayesian solution to this problem and provides approximate, implementable algorithms for both cluttered and non-cluttered scenarios involving single and multiple time-delayed measurements. Such an approach leads to a solution involving the joint probability density of current and past target states. In contrast, existing solutions in the literature modify the sensor measurement equation to account for the time delay and explicitly deal with the resulting correlations that arise in the process noise and current target state. In the Bayesian solution proposed in this paper, such cross correlations are treated implicitly. Under linear Gaussian assumptions, the Bayesian solution reduces to an augmented state Kalman filter (AS-KF) for scenarios devoid of clutter and an augmented state probabilistic data association filter (AS-PDA) for scenarios involving clutter. Computationally efficient versions of AS-KF and AS-PDA are considered in this paper. Simulations are presented to evaluate the performance of these solutions.

Moving-horizon control is a type of sampled-data feedback control in which the control over each sampling interval is determined by the solution of an open-loop optimal control problem. We develop a dual-sampling-rate moving-horizon control scheme for a class of linear, continuous-time plants with strict input saturation constraints in the presence of plant uncertainty and input disturbances. Our control scheme has two components: a slow-sampling moving-horizon controller for a nominal plant and a fast-sampling state-feedback controller whose function is to force the actual plant to emulate the nominal plant. The design of the moving-horizon controller takes into account the nonnegligible computation time required to compute the optimal control trajectory. We prove the local stability of the resulting feedback system and illustrate its performance with simulations. In these simulations, our dual-sampling-rate controller exhibits performance that is considerably superior to its single-sampling-rate moving-horizon controller counterpart.

This paper presents a new decentralised fault detection and mitigation system for excitation control systems used in synchronous generators in power grids. The system detects and diagnoses faults in the basic components of the excitation control systems with high accuracy and in almost real time using fuzzy set theory. Once a fault is diagnosed, a mitigation action follows, where the signal out of the faulty component is replaced by an alternative signal generated by the fault mitigation system. The mitigation action ensures that the field excitation voltage and thus the terminal voltage of the affected generator are controlled in a way that pre-fault stability of the grid is maintained. The accuracy and effectiveness of the proposed scheme are verified through case studies performed on the IEEE 30 bus test system.

Real-time respiratory measurement with Doppler Radar has an important advantage in the monitoring of certain conditions such as sleep apnoea, sudden infant death syndrome (SIDS), and many other general clinical uses requiring fast nonwearable and non-contact measurement of the respiratory function. In this paper, we demonstrate the feasibility of using Doppler Radar in measuring the basic respiratory frequencies (via fast Fourier transform) for four different types of breathing scenarios: normal breathing, rapid breathing, slow inhalation-fast exhalation, and fast inhalation-slow exhalation conducted in a laboratory environment. A high correlation factor was achieved between the Doppler Radar-based measurements and the conventional measurement device, a respiration strap. We also extended this work from basic signal acquisition to extracting detailed features of breathing function (I : Eratio). This facilitated additional insights into breathing activity and is likely to trigger a number of new applications in respiratory medicine.

This paper introduces a new optimal control model to describe and control the dynamics of infectious diseases. In the present model, the average time to isolation (i.e. hospitalization) of infectious population is the main time-dependent parameter that defines the spread of infection. All the preventive measures aim to decrease the average time to isolation under given constraints. The model suggested allows one to generate a “small” number of possible future scenarios and to determine corresponding trajectories of infected population in different regions. Then, this information is used to find an optimal distribution of bed capabilities across countries/regions according to each scenario.

This letter develops a sequential composite hypotheses test for detecting failure of a decision-directed recursive least square (RLS) adaptive multiuser detector (MUD) and convergence of a blind RLS (BLRS) MUD when used in the downlink of direct-sequence/code division multiple access (DS/CDMA) systems. Simulations are provided to demonstrate the effectiveness of the test and to characterize the SIR improvement achievable in a dynamic environment where the number of interferers is time-varying.

Gating techniques for maneuvering target tracking using an IMM-PDA filter are considered in this paper. Existing gating techniques, namely, centralized gating and model based gating are reviewed and new gating techniques, called model probability weighted gating and two-stage model probability weighted gating, are proposed. A benchmark trajectory and a random scenario are considered for performance evaluation of the gating techniques in terms of RMS errors, percentage of track loss and computational load.

This paper considers the problem of designing efficient and systematic importance sampling (IS) schemes for the performance study of hidden Markov model (HMM) based trackers. Importance sampling (IS) is a powerful Monte Carlo (MC) variance reduction technique, which can require orders of magnitude fewer simulation trials than ordinary MC to obtain the same specified precision. We present an IS technique applicable to error event analysis of HMM based trackers. Specifically, we use conditional IS to extend our work in another of our paper to estimate average error event probabilities. In addition, we derive upper bounds on these error probabilities, which are then used to verify the simulations. The power and accuracy of the proposed method is illustrated by application to an HMM frequency tracker.

The problem of generalised minimum variance control of linear time-varying discrete-time systems is studied. Standard time-varying controlled autoregressive moving average models are considered, and the sum of plant output tracking error variance plus a penalty term on plant input is chosen as the cost functional. The time-varying controller described is able to minimise the generalised tracking error variance and guarantees closed-loop exponential stability for a large class of linear time-varying systems, including plants which have long time delays and are not stably invertible.

The left-inverse system with minimal order of a discrete-time nonlinear system is studied in a linear algebraic framework. The general structure of the left inverse is described. Two algorithms are given for constructing left-inverse systems having minimal order.

A critical notion in the field of communication-limited control is the lowest data rate above which a given system is stabilisable. In this paper the objective is to derive this rate for a multidimensional LTI system when exponential stability with a given decay is desired. By assuming an initial state probability density, a new quantiser distortion lower bound and an asymptotic quantisation result are able to be applied to derive the infimum stabilising data rate, under very mild requirements on the initial density. Furthermore, a stabilising scheme is explicitly constructed.

Respiration detection using microwave Doppler radar has attracted significant interest primarily due to its unobtrusive form of measurement. With less preparation in comparison with attaching physical sensors on the body or wearing special clothing, Doppler radar for respiration detection and monitoring is particularly useful for long-term monitoring applications such as sleep studies (i.e. sleep apnoea, SIDS). However, motion artefacts and interference from multiple sources limit the widespread use and the scope of potential applications of this technique. Utilising the recent advances in independent component analysis (ICA) and multiple antenna configuration schemes, this work investigates the feasibility of decomposing respiratory signatures into each subject from the Doppler-based measurements. Experimental results demonstrated that FastICA is capable of separating two distinct respiratory signatures from two subjects adjacent to each other even in the presence of apnoea. In each test scenario, the separated respiratory patterns correlate closely to the reference respiration strap readings. The effectiveness of FastICA in dealing with the mixed Doppler radar respiration signals confirms its applicability in healthcare applications, especially in long-term home-based monitoring as it usually involves at least two people in the same environment (i.e. two people sleeping next to each other). Further, the use of FastICA to separate involuntary movements such as the arm swing from the respiratory signatures of a single subject was explored in a multiple antenna environment. The separated respiratory signal indeed demonstrated a high correlation with the measurements made by a respiratory strap used currently in clinical settings.

Modern semiconductor technology, such as small geometry CMOS, is providing the opportunity to build very small and very low cost single chip radar systems operating at millimeter wave frequencies. Such tiny radar systems open up a vast range of new applications from automotive radar, safety helmet radar, bicycle radar, health monitoring radar, robot guidance radar, micro-UAV surveillance radar, and a host of other applications. Co-operative networks of these tiny radars also constitute an exciting possibility. Considerable progress has been made in designing and building millimeter wave CMOS radar systems and addressing the technology limitations imposed by CMOS. However many challenges remain relating to interference mitigation when there exist large numbers of small radars operating in close spacial and frequency proximity to each other. Waveform diversity and cognitive radar techniques assist in addressing these challenges. This talk will discuss current work on CMOS radar hardware, signal processing, waveform scheduling and the idea of opportunistic radar. Further progress on approaches to determining fundamental performance limitations of tiny radar systems is currently underway.

Efficient and reliable operation of the Smart Grid largely depends on performance of the communication infrastructure. This paper investigates the opportunities to use existing public IP communication paradigm instead of constructing an end to end dedicated architecture confining to Smart Grid applications. Even though this concept is favorable as means of decreasing the installation expenditure, it might pose a challenge when ensuring Quality of Service (QoS) requirements for Smart Grid traffic. Therefore, we explore the possibility of applying prevailing service differentiation and queuing techniques at core network to improve the QoS for Smart Grid applications through a wide area communication network model incorporating public IP lines built in a simulation environment. Our results provide an examination of how performance targets are met and also a framework for utilizing the prevailing packet service mechanisms to reduce the performance impact for Smart Grid data.

Acyclic directed mixed graphs (ADMGs) are graphs that contain directed ($\rightarrow$) and bidirected ($\leftrightarrow$) edges, subject to the constraint that there are no cycles of directed edges. Such graphs may be used to represent the conditional independence structure induced by a DAG model containing hidden variables on its observed margin. The Markovian model associated with an ADMG is simply the set of distributions obeying the global Markov property, given via a simple path criterion (m-separation). We first present a factorization criterion characterizing the Markovian model that generalizes the well-known recursive factorization for DAGs. For the case of finite discrete random variables, we also provide a parameterization of the model in terms of simple conditional probabilities, and characterize its variation dependence. We show that the induced models are smooth. Consequently, Markovian ADMG models for discrete variables are curved exponential families of distributions., Comment: Published in at http://dx.doi.org/10.1214/14-AOS1206 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)

Jefferson Laboratory's Free Electron Laser (FEL) incorporates a cesiated gallium arsenide (GaAs) DC photocathode gun as its electron source. By using a set of scanning mirrors, the surface of the GaAs wafer is illuminated with a 543.5nm helium–neon laser. Measuring the current flow across the biased photocathode generates a quantum efficiency (QE) map of the 1-in. diameter wafer surface. The resulting QE map provides a very detailed picture of the efficiency of the wafer surface. By generating a QE map in a matter of minutes, the photocathode scanner has proven to be an exceptional tool in quickly determining sensitivity and availability of the photocathode for operation.

This paper considers the sensor scheduling problem which consists of estimating the state of an uncertain process based on measurements obtained by switching a given set of noisy sensors. The noise and uncertainty models considered in this paper are assumed to be unknown deterministic functions which satisfy an energy type constraint known as an integral quadratic constraint. The problem of optimal robust sensor scheduling is formulated and solution to this problem is given in terms of the existence of suitable solutions to a Riccati differential equation of the game type and a dynamic programming equation. Furthermore, a real time implementable method for sensor scheduling is also presented.

This paper investigates the stabilizability of a linear, discrete-time plant with a real-valued output when the controller, which may be nonlinear, receives observation data at a known rate. It is first shown that, under a finite horizon cost equal to the m th output moment, the problem reduces to quantizing the initial output. Asymptotic quantization theory is then applied to directly obtain the limiting coding and control scheme as the horizon approaches infinity. This is proven to minimize a particular infinite horizon cost, the value of which is derived. A necessary and sufficient condition then follows for there to exist a coding and control scheme with the specified data rate that takes the m th output moment to zero asymptotically with time. If the open-loop plant is finite-dimensional and time-invariant, this condition simplifies to an inequality involving the data rate and the unstable plant pole with greatest magnitude. Analagous results automatically hold for the related problem of state estimation with a finite data rate.

In this paper we present two new results on Bode-type integrals. Firstly, we obtain, for a given scalar or multivariable continuous-time plant, the infimum of the Bode sensitivity integral which can be obtained with any stabilizing controller. The result involves the unstable plant poles and, perhaps surprisingly, a subset of the plant nonminimum phase zeros. Secondly, we obtain an apparently new expression for the Bode integral for the complementary sensitivity for a stable discrete-time scalar system.

The driver for Jefferson Lab's kW-level infrared free-electron laser (FEL) is a superconducting, recirculating accelerator that recovers 75% of the electron-beam power and converts it to radio frequency power. As reported in FEL'98, the accelerator operated ''straight-ahead'' to deliver 38 MeV, 1.1 mA cw current for lasing at wavelengths in the vicinity of 5 microns. The waste beam was sent directly to a dump, bypassing the recirculation loop. Stable operation at up to 311 W cw was achieved in this mode. The machine has now recirculated cw average current up to 4.6 mA and has lased cw with energy recovery up to 1,720 W output at 3.1 microns. This is the first FEL to ever operate in the ''same-cell'' energy recovery mode. Energy recovery offers several advantages (reduced RF power and dramatically reduced radio-nuclide production at the dump) and several challenges will be described. The authors have observed heating effects in the mirrors which will be described. They will also report on the additional performance measurements of the FEL that have been performed and connect those measurements to standard models.

This paper considers the problem of estimating the state of a linear system via a digital link with a finite bit-rate R. Classical estimation theory does not apply here since the estimator only observes the transmitted sequence of finite-valued symbols. A new coder-estimator for linear systems with process and measurement noise is given. Provided that a certain inequality linking the bit-rate to the dynamical parameters is satisfied, and under very mild assumptions on the noise distributions, the expected absolute estimation error remains of the same order as in the classical situation with infinite bit-rate. In particular, if the classical estimation error approaches zero, then the finite bit-rate error goes to zero at exactly the same rate

As reported previously [1], Jefferson Lab is building a free-electron laser capable of generating a continuous wave kilowatt laser beam. The driver-accelerator consists of a superconducting, energy-recovery accelerator. The initial stage of the program was to produce over 100 W of average power with no recirculation. In order to provide maximum gain the initial wavelength was chosen to be 5 mu-m and the initial beam energy was chosen to be 38.5 MeV. On June 17, 1998, the laser produced 155 Watts cw power at the laser output with a 98% reflective output coupler. On July 28th, 311 Watts cw power was obtained using a 90% reflective output coupler. A summary of the commissioning activities to date as well as some novel lasing results will be summarized in this paper. Present work is concentrated on optimizing lasing at 5 mu-m, obtaining lasing at 3 mu-m, and commissioning the recirculation transport in preparation for kilowatt lasing this fall.

There are many practical control problems where the control action is determined by switching among a given set of control laws. This paper presents necessary and sufficient conditions to test for quadratic stabilizability and for robust stabilizability with a quadratic storage function for switched controller systems. Algorithms which can be used to construct appropriately stabilizing control laws are also presented.

Optical spectral monitors that monitor a range of parameters are valuable for the management of optical networks. However, they require fine spectral resolution, otherwise, the measured spectra are blurred, preventing such parameters being determined. In this letter, we demonstrate how the regularized structured total least squares signal processing method can be used to successfully reverse this blurring. Its application relaxes the required resolution for optical spectral monitors with subsequent benefits of increased robustness and reduced cost.

We investigate the properties of a fast-identification style of control algorithm applied to a class of stochastic dynamical systems in continuous time which are sampled at a constant rate. The algorithm does not assume that the system dynamics are known and estimates them using a simple filter. Under a mild smoothness condition on the system dynamics, we show that when the sampling rate is sufficiently fast, the control algorithm stabilizes the system in the sense that the sampled closed-loop system becomes an ergodic Markov chain. Moreover, an explicit bound is given for the expected deviation of the system state from the origin. The result is also adapted for the case where state-measurement is subject to random noise.

In this letter, we conduct analysis on the optimum design for RF-to-optical up-converter in coherent optical OFDM systems using an optical I/Q modulator. We first derive closed-form expressions for the nonlinearity in the optical I/Q modulator, represented by two-tone intermodulation products as a function of the bias point and modulation index. Additionally, we perform a numerical simulation to identify Q-penalty and the excess modulation insertion loss under various transmitter conditions. We find that in contrast to the direct-detected system, the optimal modulator bias point for the coherent system is pi, where the Q-penalty and excess loss are minimized

Optical channel monitors (OCMs) that use scanning filters need to resolve the amplified spontaneous emission (ASE) noise floor between channels in order to determine optical signal-to-noise ratio (OSNR). We show how regularized least-squares can extract accurate OSNR values from raw spectra when the ASE floor is not visible because of insufficient resolution in the monitor. Our experimental results demonstrate OSNR measurement accuracy to within 0.6 dB from a spectrum of 50-GHz-spaced 10-Gb/s channels measured using a scanning Fabry-Peacuterot filter with 35-GHz bandwidth. The ability to use broader bandwidth filters in OCMs without sacrificing accuracy may reduce their cost and increase their robustness

The purpose of this paper is to consider the problem of adaptive control of a continuous-time single-input single-output nonlinear stochastic system which is sampled at regular time-intervals, subject to certain input (control) constraints. The (unknown) system is not required to be stable in the absence of control. Consequently, a strict saturation input constraint cannot be imposed if the closed-loop system is to remain stable. Therefore, a type of “long-term average control effort᾿ input constraint is imposed. A control law is presented and conditions on the sampling rate are found, such that the Markov chain consisting of the state and control at the sampling instants is ergodic, and the input constraint is satisfied.

Uncertain nonlinear systems affine in the input are considered. It is shown that under some matching conditions these systems can be made practically stable by state feedback. We present an adaptive algorithm which renders the origin practically stable to any desired accuracy. The presented algorithms are simple and computationally cheap.

Importance sampling is a technique for speeding up Monte Carlo (MC) simulations. The fundamental idea is to use a different simulation distribution to increase the relative frequency of "important" events and then weight the observed data in order to obtain an unbiased estimate of the parameter of interest. This estimate often requires orders-of-magnitude fewer simulation trials than ordinary MC simulations to obtain the same specified precision. We present an importance sampling technique applicable to error event simulation of hidden Markov model (HMM) tracking algorithms. The computational savings possible with the use of this technique are demonstrated both analytically and using simulation results for a specific HMM tracking algorithm.

A new algorithm, multipath probabilistic data association (MPDA), for initiation and tracking in over-the-horizon radar (OTHR) is described. MPDA is capable of exploiting multipath target signatures arising from discrete propagation modes that are resolvable by the radar. Nonlinear measurement models exhibiting multipath target signatures in azimuth, slant range, and Doppler are used. Tracking is performed in ground coordinates and therefore depends on the provision of estimates of virtual ionospheric heights to achieve coordinate registration. Although the propagation mode characteristics are assumed to be known, their correspondence with the detections is not required to be known. A target existence model is included for automatic track maintenance. Numerical simulations for four resolvable propagation modes are presented that demonstrate the ability of the technique to initiate and maintain track at probabilities of detection of 0.4 per mode in clutter densities for which conventional probabilistic data association (PDA) has a high probability of track loss, and suffers from track bias. A nearest neighbor version of MPDA is also presented.

We provide results demonstrating the smoothness of some marginal log-linear parameterizations for distributions on multi-way contingency tables. First we give an analytical relationship between log-linear parameters defined within different margins, and use this to prove that some parameterizations are equivalent to ones already known to be smooth. Second we construct an iterative method for recovering joint probability distributions from marginal log-linear pieces, and prove its correctness in particular cases. Finally we use Markov chain theory to prove that certain cyclic conditional parameterizations are also smooth. These results are applied to show that certain conditional independence models are curved exponential families., Comment: 19 pages

Many models with latent structure are just semi-algebraic sets, and have recently begun to be studied from this perspective; this has shed much light on the dimension, identifiability, and asymptotic statistical properties of these models. Though most of the attention has been on equality constraints, some progress has also been made on evaluating inequalities which might be used to test such models. However, the mathematical complexity of these approaches seems to have led to a gap between our theoretical understanding and the manner in which these models are applied in practice. In this paper we make a plea for some focus on finding simpler (in particular more graphical) and more computationally feasible ways to express such constraints, even at the cost of a loss of statistical power. Recent advances for directed acyclic graph models with latent variables and phylogenetic models are given as illustrations.

An adaptive, waveform selective probabilistic data association (WSPDA) algorithm for tracking a single target in clutter is presented. The assumption of an optimal receiver allows the inclusion of transmitted waveform specification parameters in the tracking subsystem equations, leading to a waveform selection scheme where the next transmitted waveform parameters are selected so as to minimize the average total mean-square tracking error at the next time step. Semiclosed form solutions are given to the local (one-step-ahead) adaptive waveform selection problem for the case of one-dimensional target motion. A simple simulation example is given to compare the performance of a tracking system using a WSFDA based tracking filter with that of a conventional system with a fixed waveform shape and probabilistic data association (PDA) tracking filter.

We study the problem of minimum-variance control of linear time-varying discrete-time systems. We consider the standard time-varying controlled autoregressive moving-average model and develop a time-varying controller that guarantees both closed-loop stability and minimum-variance output control for a large class of linear time-varying systems. The controller is based on our previous results on minimum-variance prediction for linear time-varying systems. We also show that the standard minimum-variance controller cannot in general guarantee closed-loop stability when applied to linear time-varying systems. However, a simple modification can make the closed loop stable.

For original paper see Pulford and Evans (IEEE Transactions on Aerospace and Electronic Systems, vol.34, no.4, p.1165-83, 1998). The present paper replies to a comment by Liu et al. (IEEE Transactions on Aerospace and Electronic Systems, vol.41, no.3, p.1147-8, 2005). The corrections to the Jacobian matrix elements of the transformation from the ground to radar coordinates are discussed.

In this paper, we address the decision making criteria of a secondary user (SU) for deciding whether to transmit or not upon performing spectrum sensing and detecting the presence of any primary user (PU) in the environment in a cognitive radio network (CRN). We propose a reinforcement learning (RL) based approach by a Markov process at the SU node and present novel analytical methods to analyze the performance of such approaches. In particular, we define the probability of interference P i and the probability of wastage P w , and compare these metrics with a RL based and a non-RL based approach for SU transmission. The simulations show the presence of a tradeoff in the two probability metrics P w and P i , based on the Markov process. The simulation results are compared in the form of the transmitter operating characteristics (ToC) curves. Using our approach, one could control the interference to the PU by trading off with the spectral wastage.