Showing papers on "Filter design published in 2018"

Learning to Optimize: Training Deep Neural Networks for Interference Management

Abstract: Numerical optimization has played a central role in addressing key signal processing (SP) problems Highly effective methods have been developed for a large variety of SP applications such as communications, radar, filter design, and speech and image analytics, just to name a few However, optimization algorithms often entail considerable complexity, which creates a serious gap between theoretical design/analysis and real-time processing In this paper, we aim at providing a new learning-based perspective to address this challenging issue The key idea is to treat the input and output of an SP algorithm as an unknown nonlinear mapping and use a deep neural network (DNN) to approximate it If the nonlinear mapping can be learned accurately by a DNN of moderate size, then SP tasks can be performed effectively—since passing the input through a DNN only requires a small number of simple operations In our paper, we first identify a class of optimization algorithms that can be accurately approximated by a fully connected DNN Second, to demonstrate the effectiveness of the proposed approach, we apply it to approximate a popular interference management algorithm, namely, the WMMSE algorithm Extensive experiments using both synthetically generated wireless channel data and real DSL channel data have been conducted It is shown that, in practice, only a small network is sufficient to obtain high approximation accuracy, and DNNs can achieve orders of magnitude speedup in computational time compared to the state-of-the-art interference management algorithm

Fault Detection Filtering for Nonhomogeneous Markovian Jump Systems via a Fuzzy Approach

Abstract: This paper investigates the problem of the fault detection filter design for nonhomogeneous Markovian jump systems by a Takagi–Sugeno fuzzy approach. Attention is focused on the construction of a fault detection filter to ensure the estimation error dynamic stochastically stable, and the prescribed performance requirement can be satisfied. The designed fuzzy model-based fault detection filter can guarantee the sensitivity of the residual signal to faults and the robustness of the external disturbances. By using the cone complementarity linearization algorithm, the existence conditions for the design of fault detection filters are provided. Meanwhile, the error between the residual signal and the fault signal is made as small as possible. Finally, a practical application is given to illustrate the effectiveness of the proposed technique.

Event-Triggered Fault Detection Filter Design for Nonlinear Networked Systems

Abstract: This paper investigates the problem of event-triggered fault detection (FD) filter design for nonlinear networked systems in the framework of interval type-2 fuzzy systems. In the system model, the parameter uncertainty is captured effectively by the membership functions (MFs) with upper and lower bounds. For reducing the utilization of limited communication bandwidth, an event-triggered communication mechanism is applied. A novel FD filter subject to event-triggered communication mechanism, data quantization, and communication delay is designed to generate a residual signal and detect system faults, where the premise variables are different from those of the system model. Consequently, the augmented FD system is with imperfectly matched MFs, which hampers the stability analysis and FD. To relax the stability analysis and achieve a better FD performance, the information of MFs and slack matrices are utilized in the stability analysis. Finally, two examples are employed to demonstrate the effectiveness of the proposed scheme.

Transmit Waveform/Receive Filter Design for MIMO Radar With Multiple Waveform Constraints

Abstract: In this paper, we consider the joint design of both transmit waveforms and receive filters for a colocated multiple-input-multiple-output (MIMO) radar with the existence of signal-dependent interference and white noise. The design problem is formulated into a maximization of the signal-to-interference-plus-noise ratio (SINR), including various constraints on the transmit waveforms. Compared with the traditional alternating semidefinite relaxation approach, a general and flexible algorithm is proposed based on the majorization-minimization method with guaranteed monotonicity, lower computational complexity per iteration and/or convergence to a B-stationary point. Many waveform constraints can be flexibly incorporated into the algorithm with only a few modifications. Furthermore, the connection between the proposed algorithm and the alternating optimization approach is revealed. Finally, the proposed algorithm is evaluated via numerical experiments in terms of SINR performance, ambiguity function, computational time, and properties of the designed waveforms. The experiment results show that the proposed algorithms are faster in terms of running time and meanwhile achieve as good SINR performance as the the existing methods.

Distributed $H_\infty$ State Estimation for a Class of Filtering Networks With Time-Varying Switching Topologies and Packet Losses

Abstract: In this paper, the distributed ${H_{\infty }}$ state estimation problem is investigated for a class of filtering networks with time-varying switching topologies and packet losses. In the filter design, the time-varying switching topologies, partial information exchange between filters, the packet losses in transmission from the neighbor filters and the channel noises are simultaneously considered. The considered topology evolves not only over time, but also by event switches which are assumed to be subjects to a nonhomogeneous Markov chain, and its probability transition matrix is time-varying. Some novel sufficient conditions are obtained for ensuring the exponential stability in mean square and the switching topology-dependent filters are derived such that an optimal ${H_{\infty }}$ disturbance rejection attenuation level can be guaranteed for the estimation disagreement of the filtering network. Finally, simulation examples are provided to demonstrate the effectiveness of the theoretical results.

An optimal filter for short photoplethysmogram signals

Abstract: A photoplethysmogram (PPG) contains a wealth of cardiovascular system information, and with the development of wearable technology, it has become the basic technique for evaluating cardiovascular health and detecting diseases. However, due to the varying environments in which wearable devices are used and, consequently, their varying susceptibility to noise interference, effective processing of PPG signals is challenging. Thus, the aim of this study was to determine the optimal filter and filter order to be used for PPG signal processing to make the systolic and diastolic waves more salient in the filtered PPG signal using the skewness quality index. Nine types of filters with 10 different orders were used to filter 219 (2.1s) short PPG signals. The signals were divided into three categories by PPG experts according to their noise levels: excellent, acceptable, or unfit. Results show that the Chebyshev II filter can improve the PPG signal quality more effectively than other types of filters and that the optimal order for the Chebyshev II filter is the 4th order.

Event-Based Variance-Constrained ${\mathcal {H}}_{\infty }$ Filtering for Stochastic Parameter Systems Over Sensor Networks With Successive Missing Measurements

Abstract: This paper is concerned with the distributed ${\mathcal {H}}_{\infty }$ filtering problem for a class of discrete time-varying stochastic parameter systems with error variance constraints over a sensor network where the sensor outputs are subject to successive missing measurements The phenomenon of the successive missing measurements for each sensor is modeled via a sequence of mutually independent random variables obeying the Bernoulli binary distribution law To reduce the frequency of unnecessary data transmission and alleviate the communication burden, an event-triggered mechanism is introduced for the sensor node such that only some vitally important data is transmitted to its neighboring sensors when specific events occur The objective of the problem addressed is to design a time-varying filter such that both the ${\mathcal {H}}_{\infty }$ requirements and the variance constraints are guaranteed over a given finite-horizon against the random parameter matrices, successive missing measurements, and stochastic noises By recurring to stochastic analysis techniques, sufficient conditions are established to ensure the existence of the time-varying filters whose gain matrices are then explicitly characterized in term of the solutions to a series of recursive matrix inequalities A numerical simulation example is provided to illustrate the effectiveness of the developed event-triggered distributed filter design strategy

Event-Based Fault Detection Filtering for Complex Networked Jump Systems

Abstract: This paper is concerned with the fault detection filtering for complex systems over communication networks subject to nonhomogeneous Markovian parameters. A residual signal is generated that gives a satisfactory estimation of the fault, and an event-triggered scheme is proposed to determine whether the networks should be updated at the trigger instants decided by the event-threshold. Moreover, a random process is employed to model the phenomenon of malicious packet losses. Consequently, a novel method is presented to address the stochastically stability analysis and satisfies a given $H_{2}$ performance index simultaneously. The condition of the existence of the filter design algorithm is derived by a convex optimization approach to estimate the faults and to generate a residual. Finally, the proposed fault detection filtering method is then applied to an industrial nonisothermal continuous stirred tank reactor under realistic network conditions. Simulation results are given to show the effectiveness of the proposed design method and the designed filter.

Fuzzy-Affine-Model-Based Memory Filter Design of Nonlinear Systems With Time-Varying Delay

Abstract: This paper studies the piecewise-affine memory $\mathscr {H}_{\infty }$ filtering problem for nonlinear systems with time-varying delay in a delay-dependent framework. The nonlinear plant is characterized by a continuous-time Takagi–Sugeno fuzzy-affine model with parametric uncertainties. The purpose is to develop a new approach for filter synthesis procedure with less conservatism. Specifically, by constructing a novel Lyapunov–Krasovskii functional, together with a Wirtinger-based integral inequality, reciprocally convex inequality and S-procedure, an improved criterion is first attained for analyzing the $\mathscr {H}_{\infty }$ performance of the filtering error system, and then via some linearization techniques, the piecewise-affine memory filter synthesis is carried out. It is shown that the existence of desired filter gains can be explicitly determined by the solution of a convex optimization problem. Finally, simulation studies are presented to reveal the effectiveness and less conservatism of the developed approaches. It is anticipated that the proposed scheme can be further extended to the analysis and synthesis of continuous-time fuzzy-affine dynamic systems with integrated communication delays in the networked circumstance.

Fuzzy Generalized $\mathcal{H}_{2}$ Filtering for Nonlinear Discrete-Time Systems With Measurement Quantization

Abstract: In this paper, the generalized ${\mathcal {H}_{2}}$ filter design problems are addressed for a class of nonlinear discrete-time systems with measurement quantization. The considered nonlinear system is represented by Takagi–Sugeno fuzzy model and the system measurement output is quantized by a dynamic quantizer constituted by a static quantizer and a dynamic parameter before it is transmitted to the filter. The attention is focused on the design of both full- and reduced-order filters and the quantizer dynamic parameter such that the quantized filtering error systems are asymptotically stable with prescribed generalized ${\mathcal {H}_{2}}$ performances. Superior to existing results on the quantized filtering design, the proposed one is given under a unified linear matrix inequality (LMI) characterization, it is shown that the design problem can be solved if the LMIs conditions are feasible. Finally, simulation examples will be exploited to illustrate the effectiveness of the developed quantized generalized ${\mathcal {H}_{2}}$ filtering methods.

Asynchronous Filtering of Nonlinear Markov Jump Systems With Randomly Occurred Quantization via T–S Fuzzy Models

Abstract: This paper focuses on the dissipativity-based asynchronous filtering problem for a class of discrete-time Takagi–Sugeno fuzzy Markov jump systems subject to randomly occurred quantization. Considering the random fluctuations of network conditions, the randomly occurred quantization is introduced to describe the quantization phenomenon appearing in a probabilistic way. To take full advantage of the partial information of system modes for the desired system performance, we adopt the asynchronous filter in which mode transition matrix is nonhomogeneous. The mode-dependent time-varying delays are introduced, which have different bounds for different system modes. Via fuzzy-mode-dependent Lyapunov functional approach that can reduce conservatism, a sufficient condition on the existence of the asynchronous filter is derived such that the filtering error system is stochastically stable and strictly $(\mathcal{Q}, \mathcal{S},\mathcal{R})$ -dissipative. Then, the gains of the filter are obtained by solving a set of linear matrix inequalities (LMIs). An example is utilized to illustrate the validity of the developed filter design technique where the relationships among optimal dissipative performance indices, delays, quantization parameter, and the degree of asynchronous jumps are given.

Substrate Integrated Waveguide Dual-Band and Wide-Stopband Bandpass Filters

Abstract: In this letter, a new coupling structure implemented on a single-layer substrate and its application to design substrate integrated waveguide (SIW) dual-band and wide-stopband bandpass filters are presented. The TE101 and the TE102 modes of SIW cavity are used to design the proposed filters. By adjusting the dimensions of the coupling structure, the coupling coefficients of TE101 and TE102 modes can be controlled that can be used to design wide-stopband and dual-band bandpass filters. In the dual-band filter design, a slotline perturbation is used to lower the resonant frequency of TE102 mode while it has little effect on TE101 mode. To validate the proposed design approach, two SIW bandpass filters (wide-stopband and dual-band) are designed, fabricated, and measured.

Fault Detection for Linear Discrete Time-Varying Systems With Multiplicative Noise: The Finite-Horizon Case

Abstract: This paper studies the problem of fault detection for linear discrete time-varying systems with multiplicative noise in finite-horizon, where our main object is to provide an optimal fault detection filter (FDF) design scheme such that stochastic sensitivity/robustness ratio for fault diagnosis is maximized in the sense of probability 1. An operator-aided optimization approach and a generalized FDF are proposed such that solutions to the filter design issues are derived in the operator forms. The relationships among the deduced solutions are explicitly revealed via the proposed operator-aided methodology. The parameter matrices of the filter are computed in an analytical way by solving some recursive matrix equations. It is shown that the addressed approaches establish an operator-based framework of optimal FDF design for some categories of linear discrete-time systems. An example is given to illustrate the efficacy of our algorithms.

A Comparative Study of Four Kinds of Adaptive Decomposition Algorithms and Their Applications.

Abstract: The adaptive decomposition algorithm is a powerful tool for signal analysis, because it can decompose signals into several narrow-band components, which is advantageous to quantitatively evaluate signal characteristics. In this paper, we present a comparative study of four kinds of adaptive decomposition algorithms, including some algorithms deriving from empirical mode decomposition (EMD), empirical wavelet transform (EWT), variational mode decomposition (VMD) and Vold⁻Kalman filter order tracking (VKF_OT). Their principles, advantages and disadvantages, and improvements and applications to signal analyses in dynamic analysis of mechanical system and machinery fault diagnosis are showed. Examples are provided to illustrate important influence performance factors and improvements of these algorithms. Finally, we summarize applicable scopes, inapplicable scopes and some further works of these methods in respect of precise filters and rough filters. It is hoped that the paper can provide a valuable reference for application and improvement of these methods in signal processing.

Fault Detection for Nonlinear Discrete-Time Switched Systems With Persistent Dwell Time

Abstract: In this paper, the problem of fault detection filter design for a class of nonlinear switched systems is investigated. First, the nonlinear switched system is transferred into a Takagi–Sugeno fuzzy switched model by using fuzzy if-then rules. Then, based on the persistent dwell-time switching signal and the quasi-time-dependent Lyapunov function technique, an efficient condition of the performance analysis result is obtained. Based on this, a fuzzy-parameter-dependent fault detection filter is designed such that the resulting error system is globally uniformly asymptotically stable with a nonweighted $H_\infty$ performance index. Finally, simulation results are provided to show the effectiveness of the proposed technique.

Two-Dimensional Peak-to-Peak Filtering for Stochastic Fornasini–Marchesini Systems

Abstract: This paper is concerned with the problem of two-dimensional (2-D) stochastic peak-to-peak filter design for Fornasini–Marchesini local state-space (FM LSS) systems with state-multiplicative noise. First, a novel sufficient condition is established, which guarantees that the underlying FM LSS system is stochastically mean-square stable with 2-D stochastic peak-to-peak performance. A deterministic FM LSS system case is also discussed. Subsequently, based on the result, a new 2-D stochastic peak-to-peak filter of general form is designed for FM LSS systems with state-multiplicative noise. Finally, a numerical example is presented to show the effectiveness of the proposed 2-D stochastic peak-to-peak filter.

A New Methodology to Estimate the Rotating Phase of a BLDC Motor With Its Application in Variable-Speed Bearing Fault Diagnosis

Abstract: A new methodology is proposed to estimate the rotating phase of a brushless direct current (BLDC) motor under variable-speed condition through phase current analysis. Studies on this topic are limited. By adopting several signal processing techniques, including zero-phase filtering, Hilbert transform-based phase estimation, and signal truncation and alignment, an accurate rotating angle curve can be obtained from the noisy current signal featured with frequency and amplitude modulations. Particularly, a criterion called sinusoid similarity is proposed to evaluate the error of phase estimation and to guide optimal filter design. The proposed methodology is then combined with the vibration signal analysis-based order tracking technique to achieve variable-speed motor bearing fault diagnosis. Experimental results indicate that the proposed methodology provides a simple, noninvasive, highly accurate solution for the estimation of BLDC motor rotating phase, thereby presenting potential applications in motor bearing fault diagnosis and other related areas.

Design of Waveform Shaping Filter in the UFMC System

Abstract: The universal filtered multi-carrier (UFMC) has been taken as a promising candidate for future wireless communication, where a finite impulse response filter is employed to shape the waveform and enhance its resistance to inter-carrier interference. In previous studies, the Dolph–Chebyshev filter had been used due to its low-sidelobe levels, but at the cost of low flexibility to filter the performance control. Hence, this paper puts forward an effective scheme to design an anti-interference filter for UFMC system, where the Nyquist condition (or sampling inter-symbol interference equivalently), the in-band distortion, and the out-of-band emission are taken into consideration. First, this paper models the filter design as a constrained minimax optimization concerning above-mentioned filter performance indexes. Then, the original nonconvex constraints on Nyquist condition are approximately transformed to a linear matrix inequation and two linear inequations. Finally, the optimization problem is solved by semi-definite programming. The numerical examples explicitly demonstrate the flexible performance tradeoff of the proposed method, in which the included filter performance indexes can be effectively controlled. Moreover, the bit-error-rate (BER) tests of the UFMC system confirms the effectiveness of our study, where the designed filters show BER advantages over filters of the previous literature. Therefore, the proposed waveform shaping filters can benefit variant applications of UFMC for its flexible performance tradeoffs as well as the improved anti-interference performance.

An LCL -Filter Design With Optimum Total Inductance and Capacitance

Abstract: LCL -filter is among the best performing filters for grid-connected voltage source inverters Designing of the filter parameters (grid-side and inverter-side inductors and capacitor), takes an iterative approach due to the coherence between the parameters and design requirements such as IEEE-519 Std for harmonic current limitations, reactive power compensation limit, and maximum allowable voltage drop across the filter to limit the switching losses Most of the proposed LCL -filter optimization strategies emphasize on reducing the total inductance and losses of the filter while meeting the design requirements There is less emphasis given on the capacitor selection and optimizing its value Therefore, this paper proposes a method to compute the optimum capacitance requirement of the LCL -filter based on reactive power compensation of the filter rather than calculating it as a percentage of base capacitance of the filter as found in the literature The proposed design methodology compared to the previously proposed designs is capable of reducing filter capacitance by 50% while meeting the harmonic limitation demanded by IEEE-519 Std and also considers the impact of the total inductance on reactive power compensation Based on the proposed methodology an LCL -filter with minimum total inductance and capacitance is realized Functionality of the proposed LCL -filter is verified and validated through simulations and experimental results

Fault detection filter design for a class of nonlinear Markovian jumping systems with mode-dependent time-varying delays

Abstract: The fault detection filter (FDF) design problem of a class of time-delayed and nonlinear Markovian jumping systems (MJSs) is considered. The delays in this paper are mode-dependent and time-varying. Using the Takagi–Sugeno fuzzy (TSF) modeling methods, the relevant TSF-MJSs related to the TSF-FDF model are obtained. Through introducing a reference residual model, the FDF design scheme can be derived as an $$H_{\infty }$$ -filtering formulation. By selecting a suitable mode-dependent time-delayed Lyapunov–Krasovskii functional (LKF), we get sufficient conditions through which the stochastic stability of the TSF-MJSs can be guaranteed. Then in terms of linear matrix inequalities techniques, the fuzzy FDF design scheme can be derived as an optimization one. A simulation example is demonstrated as last to illustrate the feasibility of the studied methods.