Effect of F1-layer L condition on ranging accuracy for SSL HFDF systems

An extension of the multisegmented quasiparabolic model of ionospheric electron density to include the F1-layer L condition is described. Provisional results illustrating the effects of this on ranging estimates from single station location HFDF systems are discussed. It is concluded that a knowledge of actual ionospheric conditions at the time of observation are important for such systems to improve ranging estimates, especially when L conditions are present.<>     

Some estimates pertaining to sensitivity and robust stability of input-output systems

The estimates derived in this paper strengthen the available results on sensitivity and robust stability of input-output systems. Two types of estimates are discussed: the “sensitivity type”, which establishes a bound for the output change when the system is perturbed but the input remains the same, and the “robustness type”, which gives a bound for the output change when the input changes but the perturbation does not. First, estimates for general systems over abstract extended spaces are derived; these results are then applied to (1) two frequently used control configurations, and (2) systems governed by vector integral and differential equations on the time domain [0, ∞). The applications of the estimates are illustrated by several examples. This research was supported by the National Science Foundation under Grant #DMS-9102910     

Gradient estimation for stochastic optimization of opticalcode-division multiple-access systems .I. Generalized sensitivityanalysis

For optimizing the performance of optical code-division multiple-access (CDMA) systems, there is a need for determining the sensitivity of the bit-error rate (BER) of the system to various system parameters. Asymptotic approximations and bounds, used for system bit-error probabilities, seldom capture the sensitivities of the system performance. We develop single-run gradient estimation methods for such optical CDMA systems using a discrete-event dynamic systems (DEDS) approach. Specifically, computer-aided techniques such as infinitesimal perturbation analysis (IPA) and likelihood ratio (LR) methods are used for analyzing the sensitivity of the average BER to a wide class of system parameters. It is shown that the above formulation is equally applicable to time-encoded and frequency-encoded systems. Further, the estimates derived are unbiased, and also optimality of the variance of these estimates is shown via the theory of common random variates and importance sampling techniques     

Statistical analysis of a power-law model for repair data

The power-law process is an alternative model to the homogeneous Poisson process for analyzing repair data. When many nominally-identical systems are in service, the repair data for all systems can be used to assess the aptness of the power-law model. To test the hypothesis of a homogeneous Poisson process versus the power-law process, the maximum likelihood estimates of the power-law process parameters are used. A table of the critical values for the estimates is given, along with an example of their use     

多传感器分布式推广卡尔曼滤波算法及其在雷达红外目标跟踪中的应用

本文提出了一种用于非线性系统的多传感器分布式推广卡尔曼滤波算法,该算法中系统的动态方程和传感器的观测方程分别围绕全局估计和全局预测线性化,融合中心基于所有传感器观测的全局估计由各传感器基于自身观测的局部估计来重构。算法分析说明,全局估计的精度高、误差小。最后介绍了文中算法在雷达和红外两种传感器跟踪机动目标中的应用,仿真结果验证了该算法的有效性。     

Undersea fiber optic cable communications system of the future: Operational, reliability, and systems considerations

The systems and operational requirements, reliability, and cost estimates for several undersea fiber optic cable communications systems of varying capacities, data transmission rates, and link distances are analyzed in detail to highlight relevant design tradeoff parameters. It is shown that systems with a high data rate per fiber are more economical and reliable than low data rate systems with multiple fibers. This study concludes that undersea optic cable systems can soon provide reliable broad-band digital services.     

A Survey of Approaches to Solving Inverse Problems for Lossless Layered Media Systems

In this paper, we survey approaches to solving inverse problems for lossless layered systems. Such systems can be modeled in two different ways, by nonparametric or parametric models. We review both models, but concentrate our attention on inverse and estimation procedures for parametric models. Algebraic inverse procedures are described for determining the reflection coefficient parameters when measurements are noise free. An extension of these procedures to the case of noisy data is also discussed; but, resulting reflection coefficient values are suboptimal. Finally, we describe two procedures for estimating reflection coefficients from noisy data. One of these, which is very promising, is a maximum-likelihood procedure, which is not only able to provide estimates of reflection coefficients, but is also able to provide estimates of another set of parameters, layer travel times. These maximum-likelihood estimates are optimal.     

A statistical approach to subspace based blind identification

Blind identification of single input multiple output systems is considered herein. The low-rank structure of the output signal is exploited to blindly identify the channel using a subspace fitting framework. Two approaches based on a minimal linear parameterization of a subspace are presented and analyzed. The asymptotically best consistent estimate is derived for the class of blind subspace-based techniques. The asymptotic estimation error covariance of the subspace estimates is derived, and the corresponding covariance of the statistically optimal estimates provides a lower bound on the estimation error covariance of subspace methods. A two-step procedure involving only linear systems of equations is presented that asymptotically achieves the bound. Simulations and numerical examples are provided to compare the two approaches     

Smooth perturbation on a classical energy function for lossy power system stability analysis

The efforts to find Lyapunov functions for power systems with losses have been until now in vain. Despite that, engineers have been using approximated energy-like functions to obtain good estimates of the critical clearing time (CCT) in transient stability analysis of power systems. These approximated energy-like functions are not Lyapunov functions, and are usually obtained by an integration process followed by an approximation of the integration path. Therefore, the good CCT estimates obtained with these functions are not supported by a sound theory. Nevertheless, it is shown in this paper, for a particular approximated energy-like function, a theoretical approach to support these good estimates. The approximated energy-like function studied in this paper is well known in the literature, and was proposed by Athay et al. in the COA formulation. It is shown that this approximated energy-like function is neither a Lyapunov function in the usual sense, nor an extended Lyapunov function, when the transfer conductances are taken into account. In spite of that, a function attending the requirements of the extension of the Invariance Principle, that is, an extended Lyapunov function, can be obtained by smooth perturbations on that energy-like function. This perturbed function can be used to estimate the attraction area without approximations or conjectures. Indeed, the difference between the proposed extended Lyapunov function and the approximated energy-like function has the order of a smooth perturbation. This fact supports the good CCT estimates that have been obtained using these approximated energy-like functions, and encourages engineers to keep using them for CCT estimates.     

Importance sampling for analysis of direct detection opticalcommunication systems

Analytical solutions of the performance of optical communication systems are difficult to obtain and often, Monte Carlo simulations are used to achieve realistic estimates of the performance of such systems. However, for high performance systems, this technique requires a large number of simulation trials for the estimates to be in a reasonable interval of confidence, with the number of trials increasing linearly with the performance of the system. We apply an importance sampling technique to estimate the performance of direct detection optical systems, where the “gain” of importance sampling over Monte Carlo simulations is shown to increase linearly with the system performance. Further, we use this technique to study the performance of optical communication systems employing avalanche photodetectors as well as fibre-optic code division multiple access systems (FO-CDMA). We also show that the quick simulation technique developed can be used for a wide variety of coding schemes, and for the first time, we present a comparative analysis of the performance of FO-CDMA systems employing optical orthogonal codes and prime sequences. In all cases, it is shown that importance sampling simulations require less than 50-100 trials for estimating error probabilities of 10^-10 and below     

Performance Monitoring of Digital Communication Systems Using Extreme-Value Theory

Extreme-value theory has been suggested by various investigators as an efficient tool for use in monitoring the performance of digital communication systems. One drawback to such a procedure is that it requires learning periods to estimate certain parameters of the extremal distributions. Clearly, whenever an update of the estimates is needed, transmission of information must temporarily cease since a new learning period must begin. To avoid this problem, a technique is proposed for obtaining estimates of the parameters when the learning period is eliminated (i.e., estimates are obtained using noisy samples), and these parameter estimates are in turn used to estimate the probability of error of the communication system. The accuracy of this procedure is tested by computer simulation, and it is indicated how one can verify (in a statistical sense) whether or not this technique is appropriate for any given system.     

Distributed Weighted Fusion Estimators with Random Delays and Packet Dropping

This paper is concerned with the distributed fusion estimation in sensor networks where local estimates are sent to a fusion centre for fusion estimation, with random delays and packet dropouts. Under the linear minimum variance sense, a distributed optimal weighted fusion estimator is given for discrete time-invariant stochastic linear systems with multiple distributed sensors. The algorithm involves a weighted fusion of local predictors with different prediction steps from different sensor sources. A recursive computation of the cross-covariance matrix of prediction errors between any two local estimates is derived. We present two fusion strategies. One is to fuse the latest local estimates that reach the fusion centre at the current time. The other is to fuse the latest local estimates that reach the fusion centre at the current time and the predicted estimates of those that do not have estimates received at the current time. Further, to reduce the computation cost, only the local estimates satisfying the given precision requirement are fused because those with longer delays or consecutive packet dropouts have large estimation errors. A strategy to select local estimators for fusion is presented based on gate thresholds of time delays or the numbers of consecutive packet dropouts for all local estimators. This method can be implemented offline. Simulation for a tracking system with four sensors shows the effectiveness of the proposed approaches.     

Nonlinear estimation with quantized measurements--PCM, predictive quantization, and data compression

Statistics conditioned on quantized measurements are considered in the general case. These results are specialized to Gaussian parameters and then extended to discrete-time linear systems. The conditional mean of the system's state vector may be found by passing the conditional mean of the measurement history through the Kalman filter that would be used had the measurements been linear. Repetitive use of Bayes' rule is not required. Because the implementation of this result requires lengthy numerical quadrature, two approximations are considered: the first is a power-series expansion of the probablity-density function; the second is a discrete-time version of a previously proposed algorithm that assumes the conditional distribution is normal. Both algorithms may be used with any memory length on stationary or nonstationary data. The two algorithms are applied to the noiseless-channel versions of the PCM, predictive quantization, and predictive-comparison data compression systems; ensemble-average performance estimates of the nonlinear filters are derived. Simulation results show that the performance estimates are quite accurate for most of the cases tested.     

Space–Time Precoding for Mean and Covariance Feedback: Application to Wideband OFDM

Space–Time Precoding for Mean and Covariance Feedback: Application to Wideband OFDM We consider optimization of the capacity of a multi-input single-output wideband cellular “downlink,” in which the base station has estimates of the statistics of the spatial channel. Our main focus is on orthogonal frequency-division multiplexed systems, although some of our results apply to single-carrier systems as well. Prior work has shown that estimates of the channel spatial covariance can be obtained without overhead for both frequency-division duplex (FDD) and time-division duplex (TDD) systems by suitably averaging uplink measurements. In this paper, we investigate the benefits of supplementing this “free” covariance feedback with mean feedback, where the latter refers to estimates of the spatial channel realization in each subcarrier. Mean feedback can be obtained using reciprocity for TDD systems, and requires explicit feedback for FDD systems. We first devise strategies for using both covariance and mean feedback, mainly restricting attention to beamforming, which is optimal or near-optimal for many outdoor channels with narrow spatial spread. Second, since mean feedback degrades rapidly with feedback delay for mobile channels, we develop quantitative rules of thumb regarding the accuracy required for the mean feedback to be a useful supplement to the already available, and robust, covariance feedback. Our results validate the following intuition: the accuracy requirements for mean feedback to be useful are more relaxed for channels with larger spatial spread, or for a larger number of transmit elements.     

Variance of system-reliability estimates with arbitrarily repeatedcomponents

A model is developed to determine the variance of system reliability estimates and to estimate confidence intervals for series-parallel systems with arbitrarily repeated components. For these systems, different copies of the same component-type are used several or many times within the system, but only a single reliability estimate is available for each distinct component-type. The single estimate is used everywhere the component appears in the system design, and component estimation-error is then magnified at the system-level. The "system-reliability estimate" variance and confidence intervals are derived when the number of component failures follow the binomial distribution with an unknown, yet estimable, probability of failure. The "system-reliability estimate" variance and confidence intervals are obtained by expressing system reliability as a linear sum of products of higher order moments for component unreliability. The generating function is used to determine the moments of the component-unreliability estimates. This model is preferable for many system reliability estimation problems because it does not require independent component and subsystem reliability estimates; it is demonstrated with an example     

Robust motion/force control of uncertain holonomic/nonholonomic mechanical systems

In this paper, robust control strategies are presented systematically for both holonomic mechanical systems and a large class of nonholonomic mechanical systems in the presence of uncertainties and disturbances. First, robust control strategies are presented for both kinds of systems using the bounds of system parameters, respectively. Then, adaptive robust control strategies are presented by tuning the parameter estimates online. Proportional plus integral feedback control is used for force control for the benefit of real-time implementation. The proposed control strategies guarantee that the system motion converges to the desired manifold with prescribed performance while the constraint force remains bounded.     

Space-time precoding for mean and covariance feedback: application to wideband OFDM

We consider optimization of the capacity of a multi-input single-output wideband cellular "downlink," in which the base station has estimates of the statistics of the spatial channel. Our main focus is on orthogonal frequency-division multiplexed (OFDM) systems, although some of our results apply to single-carrier systems, as well. Prior work has shown that estimates of the channel spatial covariance can be obtained without overhead for both frequency-division duplex (FDD) and time-division duplex (TDD) systems by suitably averaging uplink measurements. In this paper, we investigate the benefits of supplementing this "free" covariance feedback with mean feedback, where the latter refers to estimates of the spatial channel realization in each subcarrier. Mean feedback can be obtained using reciprocity for TDD systems, and requires explicit feedback for FDD systems. We first devise strategies for using both covariance and mean feedback, mainly restricting attention to beamforming, which is optimal or near-optimal for many outdoor channels with narrow spatial spread. Second, since mean feedback degrades rapidly with feedback delay for mobile channels, we develop quantitative rules of thumb regarding the accuracy required for the mean feedback to be a useful supplement to the already available, and robust, covariance feedback. Our results validate the following intuition: the accuracy requirements for mean feedback to be useful are more relaxed for channels with larger spatial spread, or for a larger number of transmit elements.     

Online system identification using matrix pseudoinverse

Recursive algorithms for online identification of discrete-time systems have been described. These provide minimum-norm estimates of the parameter vector when insufficient data are available, and least-squares estimates with sufficient data. Matrix inversion is not required; nor is the a priori knowledge of noise statistics or probability densities of the parameters.     

A combined timing and frequency synchronization and channel estimation for OFDM

This letter addresses training-signal-based combined timing and frequency synchronization and channel estimation for orthogonal frequency-division multiplexing systems. The proposed scheme consists of two stages. At the first stage, coarse timing and frequency-offset estimates are obtained. Based on these estimates, a (coarse) channel response estimate is obtained. The timing and frequency-offset estimates at the second stage are obtained by maximum-likelihood (ML) realization based on a sliding observation vector. Then ML channel estimation is performed. A means of complexity reduction by an adaptive scheme is also presented. The simulation results show that the proposed combined approach performs quite well, and circumvents the problem of mismatch among individual synchronization tasks.