Recursive Distributed Secure State Estimation for Nonlinear Systems With Amplify-and-Forward Relays and Stochastic Measurement Delays Over Sensor Network

The distributed and centralized fusion filtering problems for multi-sensor networked systems with transmission random one-step delays and non-consecutive packet losses are addressed. The signal evolution model is not required, as only covariance information is used. The measurements of individual sensors, subject to uncertainties modeled by random matrices and correlated noises, are transmitted to local processors through different communication channels and, due to random transmission failures, some of the data packets may be delayed or even definitely lost. The random transmission delays and non-consecutive packet losses are modeled by sequences of Bernoulli variables with different probabilities. By an innovation approach, local least squares linear filtering estimators are obtained by recursive algorithms; the distributed fusion framework is then used to obtain the optimal matrix-weighted combination of the local filters, using the mean squared error as optimality criterion. Also, a recursive least squares linear estimation algorithm is designed within the centralized fusion framework.

In this paper a robust adaptive pole placement method for a class of linear parameter varying (LPV) system based on input-output description is constructed after the LPV system model, including its un-modeled error model term, is presented. The recursive least square estimation algorithm with dead zone is applied for the parameter estimation. The robust stability of closed-loop system is analyzed and the robust bound is derived. One simulation example illustrates the effectiveness of the control algorithm and demonstrates that the adaptive control based on LPV model can achieve better performance than the controller based on linear time varying (LTV) model

A method is introduced for improving the tracking or identification behaviour of recursive parameter estimation algorithms. The method utilizes available, measured information from the process or information which acts on the process. The information is processed by a nonlinear, dynamic precontrol algorithm. Measurement updates are transformed into approximate parameter updates that are used as reference values in the classical estimation algorithm. Aircraft control has been chosen as an application example, and 6-degree-of-freedom simulation results are excellent.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

This paper addresses the collaborative localization problem for unmanned surface vehicle (USV) clusters with random measurement delays. We propose a Cubature Kalman Hybrid Consensus Filter (CKHCF) based on the cubature Kalman filter (CKF) for widely distributed USV clusters lacking global communication capabilities. In this approach, each USV exchanges two pairs of information with all its neighbors and recalculates the received localization data based on distance and relative angle measurements. The recalculated information is then fused with the locally filtered data and updated to obtain localization information based on global measurements. To mitigate the impact of random measurement delays, we employ one-step prediction to compensate for delayed measurements. We present the derivation of the CKHCF algorithm and prove its consistency and boundedness using mathematical induction. Finally, we validate the effectiveness of the proposed algorithm through simulation experiments.

Making use of the general physical model of the Mach-Zehnder interferometer with photon loss which is a fundamental physical issue, we investigate the continuous-variable quantum phase estimation based on machine learning approach, and an efficient recursive Bayesian estimation algorithm for Gaussian states phase estimation has been proposed. With the proposed algorithm, the performance of the phase estimation may be improved distinguishably. For example, the physical limits (i.e., the standard quantum limit and Heisenberg limit) for the phase estimation precision may be reached in more efficient ways especially in the situation of the prior information being employed, the range for the estimated phase parameter can be extended from [0, π/2] to [0, 2π] compared with the conventional approach, and influences of the photon losses on the output parameter estimation precision may be suppressed dramatically in terms of saturating the lossy bound. In addition, the proposed algorithm can be extended to the time-variable or multi-parameter estimation framework.

In this thesis, we formulate Wiener and Kalman filtering for the optimal estimation and prediction of self-similar processes. Our algorithms are based on the scale stationary processes framework which provides a mathematically tractable system theoretic model enabling the development of many signal processing techniques. In this framework, self-similar processes are modeled using scale stationary Auto Regressive Moving Average (ARMA) models. It has been shown that such continuous-time ARMA systems can be processed by generalized Mellin and Scale Transforms, analogous to the use of the Laplace and Fourier Transforms in the continuous-time processing of ordinary stationary ARMA systems. In this work, we first attempt to discretize these models by using exponential sampling and then formulate discrete time algorithms for discrete time processing. These discrete time techniques are used in the implementation of the optimal Wiener filtering algorithm obtained for self-similar processes using least squares techniques. Wiener filtering algorithm in Discrete Generalized Scale Transform (DGST) domain is defined in terms of scale power spectral densities. Hence, we develop a periodogram-like method for the estimation of the power spectrum of self-similar processes using DGST. Wiener filtering algorithm for the restoration of self-similar processes is tested on simulation examples. Investigation of the state space analysis is needed to fully develop the self-similar ARMA processes framework. First, a general state space representation of self-similar ARMA processes is obtained based on first order time varying ordinary differential equations. It is shown that the major difference of this representation is in the scale memory content. Here, we introduced new concepts, i.e., the "multivariate self-similarity" of the states which is captured in the "self-similarity matrix". In this state space representation, the self-similarity of the outputs is represented as a linear combination of self-similar states having different self-similarity parameters. Secondly, we develop a recursive predictive estimation algorithm in the form of a Kalman filter for self-similar processes using the proposed state space representation. Simulation examples suggest that the proposed algorithm is superior to the traditional Kalman filtering technique when the input and the output processes are self-similar.

The lifted state-space models for a class of multirate systems non-uniformly sampled from their continuous-time systems are derived, and the corresponding input-output relationship is obtained. For unmeasurable information vectors arising in the identification models, this paper gives an auxiliary-model-based recursive least-squares (AM-RLS) identification algorithm to estimate the parameters of non-uniformly sampled data systems using the auxiliary model method. The basic idea is to replace the unknown inner variables in the information vector with the outputs of the auxiliary model. Convergence properties of the algorithm proposed show that the parameter estimation error consistently converges to zero under the generalized excitation condition and bounded noise variance. A simulation example is included.

The exponentially weighted moving average (EWMA) model is a particular modeling scheme, supported by RiskMetrics, that is capable of forecasting the current level of volatility of financial time series. It is designed to track changes in the conditional variance of financial returns by assigning exponentially decreasing weights to observed past squared measurements. The aim of this paper is twofold. First, it introduces two recursive estimation algorithms that are appropriate for the EWMA model. Both are derived by employing the general recursive prediction error scheme. Moreover, they represent a computationally effective alternative to already established nonrecursive estimation strategies since they are effective in terms of memory storage, computational complexity and detecting structural changes. Second, this paper investigates the prediction ability of the proposed recursive estimation schemes when compared with other common (nonrecursive) estimation methods. The priorities of the suggested recursive estimators are demonstrated by means of a simulation study and an extensive empirical case study of eighteen key world stock indexes. Combinations of recursive predictions are also studied. Such a strategy can be recommended due to its advantageous properties when predicting volatility.

This paper designs the event-triggered non-fragile state estimator for a class of time-delayed complex networks with randomly occurring sensor saturations (ROSSs) and estimator gain variations on the basis of measurements from partial nodes. Both the time-varying state delays and the stochastic disturbances are considered in the network model. A Bernoulli-distributed white sequence is utilized to reflect the phenomenon of ROSSs. Two sequences of Gaussian distributed random variables combined with the multiplicative norm-bounded uncertainties are used to characterize the randomly occurring gain variations in the estimators. An event generator function is employed to regulate the transmission of data from the sensor to the estimator. The aim of this paper is to design an exponentially ultimately bounded state estimator in mean square through measurement outputs from a partial of network nodes under the event-triggered mechanism. With the help of Lyapunov–Krasovskii functional and stochastic analysis techniques, sufficient conditions are acquired for the existence of the desired state estimator which ensures that the estimation error dynamics is exponentially ultimately bounded in mean square, and then the estimator gain matrices can be computed via the software Matlab. A simulation example is provided to demonstrate the effectiveness of the proposed state estimation method.

Application of Neural Networks to Rudder Roll Stabilization

In this paper, the event-based state and unknown input estimation (SUIE) problem is investigated for a class of stochastic systems subject to parameter uncertainties and stochastic nonlinearities. For the purpose of reducing the energy consumption in data transmission, an event-triggering protocol is employed to regulate whether the current measurement is transmitted by the sensor. Utilising the event-triggered measurement, a recursive estimator is constructed to concurrently estimate the state and the unknown input. The upper bounds of estimation error covariances are given explicitly for both the state and the unknown input estimates. By means of the completing-the-square technique and Lagrange multiplier method, the estimator gain matrices are designed which minimise the obtained upper bounds. Finally, a numerical example is given to show the effectiveness of the proposed SUIE method.

A spectral estimation technique is presented for autoregressive moving-average (ARMA) processes. The technique is based on a parameter estimation technique known as the rec ursive maximum likelihood (RML) method. The recursive spectral estimation algorithm is presented and its asymptotic properties are discussed. Simulation results are presented to illustrate the performance of the estimator for various types of data.

This study is concerned with the state smoothing problem for a class of non-linear discrete-time stochastic systems with one-step random measurement delay (ORMD). The main contribution is that the forward–backward particle smoothing scheme is successfully extended to the systems with ORMD. First, the particle filter, specially designed to deal with ORMD, is implemented to obtain the filtering distribution and the joint distribution of state history. Then, by marginalising the joint distribution, the one-step fixed-lag smoothing distribution can be obtained. Finally, based on the forward–backward smoothing scheme, the particle approximation of the fixed-interval smoothing distribution can be obtained by re-weighting the particles which have been used in the foregoing one-step fixed-lag smoothing distribution. Simulation results demonstrate the effectiveness of the proposed smoother.

Indirect adaptive controller based on a self-structuring fuzzy system for nonlinear modeling and controlIn this paper, a unified nonlinear modeling and control scheme is presented. A self-structuring Takagi-Sugeno (T-S) fuzzy model is used to approximate the unknown nonlinear plant based on I/O data collected on-line. Both the structure and the parameters of the T-S fuzzy model are updated by an on-line clustering method and a recursive least squares estimation (RLSE) algorithm. The rules of the fuzzy model can be added, replaced or deleted on-line to allow a more flexible and compact model structure. The overall controller consists of an indirect adaptive controller and a supervisory controller. The former is the dominant controller, which maintains the closed-loop stability when the fuzzy system is a good approximation of the nonlinear plant. The latter is an auxiliary controller, which is activated when the tracking error reaches the boundary of a predefined constraint set. It is proven that global stability of the closed-loop system is guaranteed in the sense that all the closed-loop signals are bounded and simulation examples demonstrate the effectiveness of the proposed control scheme.

This paper proposes a new approach for global motion estimation using recursive algorithm in the sparsely sampled field, as well as we process parametric estimation in framework of one stage not in proposed pyramid structure. Firstly, we divide the image into blocks and obtain the highest gradient magnitude in each block to form a sparsely sampled field. Then, we derive a new recursive gradient-based algorithm for global motion estimation in sparsely sampled field. The low pass filtering is for eliminating noise of original images before the estimation processes. Finally, we propose one stage framework for the parametric refinement without the proposed hierarchical configuration. The simulation results show the comparisons of performance between our method and others.