Robust filtering by fictitious noises

Author name used in this publication: David ZhangVersion of RecordPublishe

Robust Kalman filtering for discrete time-varying uncertain systems with multiplicative noises

Copyright [2002] IEEE. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Brunel University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected]. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.In this paper, a robust finite-horizon Kalman filter is designed for discrete time-varying uncertain systems with both additive and multiplicative noises. The system under consideration is subject to both deterministic and stochastic uncertainties. Sufficient conditions for the filter to guarantee an optimized upper bound on the state estimation error variance for admissible uncertainties are established in terms of two discrete Riccati difference equations. A numerical example is given to show the applicability of the presented method

Deconvolution Filtering for Nonlinear Stochastic Systems with Randomly Occurring Sensor Delays via Probability-Dependent Method

This paper deals with a robust H∞ deconvolution filtering problem for discrete-time nonlinear stochastic systems with randomly occurring sensor delays. The delayed measurements are assumed to occur in a random way characterized by a random variable sequence following the Bernoulli distribution with time-varying probability. The purpose is to design an H∞ deconvolution filter such that, for all the admissible randomly occurring sensor delays, nonlinear disturbances, and external noises, the input signal distorted by the transmission channel could be recovered to a specified extent. By utilizing the constructed Lyapunov functional relying on the time-varying probability parameters, the desired sufficient criteria are derived. The proposed H∞ deconvolution filter parameters include not only the fixed gains obtained by solving a convex optimization problem but also the online measurable time-varying probability. When the time-varying sensor delays occur randomly with a time-varying probability sequence, the proposed gain-scheduled filtering algorithm is very effective. The obtained design algorithm is finally verified in the light of simulation examples

Signal estimation using H [infinity sign] criteria

In many signal processing and communication (SPC) applications we require to estimate signal corrupted by channel and additive noise. Optimal linear filters and predictors are used to recover signal from given observed (corrupted) signal. Kalman and Wiener filters are commonly used as optimal filters. These filters minimize the mean square error (MSE) or variance of the output error. The minimization require exact knowledge of input signal and noise power spectral density (PSD). Therefore, the performance of Kalman or Wiener filters degrade if the input signal and noise statistics is changing with time and is not known a priori. In many SPC applications there is no exact knowledge of the input signal and noise Statistics and Probability; One solution to this is to use the filters which minimizes MSE and adapt to changing input signals and noise Statistics and Probability; This solution falls into a general category of adaptive filters. Often, convergence speed of the adaptive filter algorithm determines the performance as it is assumed that the convergence speed is fast enough to track the changes in the input signal and noise Statistics and Probability; If the convergence speed is not able to track the input signal and noise statistics one can expect large variation in the output error power. Another approach to overcome unknown input signal and noise statistics is to use the mini-max estimation. One approach towards mini-max estimation is to minimize the error using H[infinity] criteria to obtain H[infinity] filters. This will lead to a conservative (minimize over the worst case input signals) design that is more robust to changes in the input signal and noise Statistics and Probability;;In this dissertation, interpretation of H[infinity] filters for zero mean stationary signals is discussed. From this H[infinity] filters are represented in the time and frequency domain. Performance benefits of H[infinity] filters over minimum variance filters are derived from this representation. Mathematical solutions to compute sub-optimal H[infinity] filters in time and frequency domain are discussed. Finally, performance benefits of H[infinity] filters for the code division multiple access (CDMA) system, signal estimation problems, and adaptive filters are shown through simulation results

Robust Techniques for Signal Processing: A Survey

Coordinated Science Laboratory was formerly known as Control Systems LaboratoryU.S. Army Research Office / DAAG29-81-K-0062U.S. Air Force Office of Scientific Research / AFOSR 82-0022Joint Services Electronics Program / N00014-84-C-0149National Science Foundation / ECS-82-12080U.S. Office of Naval Research / N00014-80-K-0945 and N00014-81-K-001

Recursive Estimation for Dynamical Systems with Different Delay Rates Sensor Network and Autocorrelated Process Noises

The recursive estimation problem is studied for a class of uncertain dynamical systems with different delay rates sensor network and autocorrelated process noises. The process noises are assumed to be autocorrelated across time and the autocorrelation property is described by the covariances between different time instants. The system model under consideration is subject to multiplicative noises or stochastic uncertainties. The sensor delay phenomenon occurs in a random way and each sensor in the sensor network has an individual delay rate which is characterized by a binary switching sequence obeying a conditional probability distribution. By using the orthogonal projection theorem and an innovation analysis approach, the desired recursive robust estimators including recursive robust filter, predictor, and smoother are obtained. Simulation results are provided to demonstrate the effectiveness of the proposed approaches

Generative adversarial networks review in earthquake-related engineering fields

Within seismology, geology, civil and structural engineering, deep learning (DL), especially via generative adversarial networks (GANs), represents an innovative, engaging, and advantageous way to generate reliable synthetic data that represent actual samples' characteristics, providing a handy data augmentation tool. Indeed, in many practical applications, obtaining a significant number of high-quality information is demanding. Data augmentation is generally based on artificial intelligence (AI) and machine learning data-driven models. The DL GAN-based data augmentation approach for generating synthetic seismic signals revolutionized the current data augmentation paradigm. This study delivers a critical state-of-art review, explaining recent research into AI-based GAN synthetic generation of ground motion signals or seismic events, and also with a comprehensive insight into seismic-related geophysical studies. This study may be relevant, especially for the earth and planetary science, geology and seismology, oil and gas exploration, and on the other hand for assessing the seismic response of buildings and infrastructures, seismic detection tasks, and general structural and civil engineering applications. Furthermore, highlighting the strengths and limitations of the current studies on adversarial learning applied to seismology may help to guide research efforts in the next future toward the most promising directions