Showing papers on "Filter design published in 2019"

Visual Tracking via Adaptive Spatially-Regularized Correlation Filters

Abstract: In this work, we propose a novel adaptive spatially-regularized correlation filters (ASRCF) model to simultaneously optimize the filter coefficients and the spatial regularization weight. First, this adaptive spatial regularization scheme could learn an effective spatial weight for a specific object and its appearance variations, and therefore result in more reliable filter coefficients during the tracking process. Second, our ASRCF model can be effectively optimized based on the alternating direction method of multipliers, where each subproblem has the closed-from solution. Third, our tracker applies two kinds of CF models to estimate the location and scale respectively. The location CF model exploits ensembles of shallow and deep features to determine the optimal position accurately. The scale CF model works on multi-scale shallow features to estimate the optimal scale efficiently. Extensive experiments on five recent benchmarks show that our tracker performs favorably against many state-of-the-art algorithms, with real-time performance of 28fps.

On the Uplink Max–Min SINR of Cell-Free Massive MIMO Systems

Abstract: A cell-free massive multiple-input multiple-output system is considered using a max-min approach to maximize the minimum user rate with per-user power constraints. First, an approximated uplink user rate is derived based on channel statistics. Then, the original max-min signal-to-interference-plus-noise ratio problem is formulated for the optimization of receiver filter coefficients at a central processing unit and user power allocation. To solve this max-min non-convex problem, we decouple the original problem into two sub-problems, namely, receiver filter coefficient design and power allocation. The receiver filter coefficient design is formulated as a generalized Eigenvalue problem, whereas the geometric programming (GP) is used to solve the user power allocation problem. Based on these two sub-problems, an iterative algorithm is proposed, in which both problems are alternately solved while one of the design variables is fixed. This iterative algorithm obtains a globally optimum solution, whose optimality is proved through establishing an uplink-downlink duality. Moreover, we present a novel sub-optimal scheme which provides a GP formulation to efficiently and globally maximize the minimum uplink user rate. The numerical results demonstrate that the proposed scheme substantially outperforms the existing schemes in the literature.

Adaptive Consensus-Based Distributed Target Tracking With Dynamic Cluster in Sensor Networks

Abstract: This paper is concerned with the target tracking problem over a filtering network with dynamic cluster and data fusion. A novel distributed consensus-based adaptive Kalman estimation is developed to track a linear moving target. Both optimal filtering gain and average disagreement of the estimates are considered in the filter design. In order to estimate the states of the target more precisely, an optimal Kalman gain is obtained by minimizing the mean-squared estimation error. An adaptive consensus factor is employed to adjust the optimal gain as well as to acquire a better filtering performance. In the filter’s information exchange, dynamic cluster selection and two-stage hierarchical fusion structure are employed to get more accurate estimation. At the first stage, every sensor collects information from its neighbors and runs the Kalman estimation algorithm to obtain a local estimate of system states. At the second stage, each local sensor sends its estimate to the cluster head to get a fused estimation. Finally, an illustrative example is presented to validate the effectiveness of the proposed scheme.

Max–Min Rate of Cell-Free Massive MIMO Uplink With Optimal Uniform Quantization

Abstract: Cell-free massive multiple-input–multiple-output (MIMO) is considered, where distributed access points (APs) multiply the received signal by the conjugate of the estimated channel, and send back a quantized version of this weighted signal to a central processing unit (CPU). For the first time, we present a performance comparison between the case of perfect fronthaul links, the case when the quantized version of the estimated channel and the quantized signal are available at the CPU, and the case when only the quantized weighted signal is available at the CPU. The Bussgang decomposition is used to model the effect of quantization. The max–min problem is studied, where the minimum rate is maximized with the power and fronthaul capacity constraints. To deal with the non-convex problem, the original problem is decomposed into two sub-problems (referred to as receiver filter design and power allocation). Geometric programming (GP) is exploited to solve the power allocation problem whereas a generalized eigenvalue problem is solved to design the receiver filter. An iterative scheme is developed and the optimality of the proposed algorithm is proved through uplink–downlink duality. A user assignment algorithm is proposed which significantly improves the performance. The numerical results demonstrate the superiority of the proposed schemes.

Hidden-Markov-Model-Based Asynchronous Filter Design of Nonlinear Markov Jump Systems in Continuous-Time Domain

Abstract: This paper addresses the dissipative asynchronous filtering problem for a class of Takagi–Sugeno fuzzy Markov jump systems in the continuous-time domain. The hidden Markov model is applied to describe the asynchronous situation between the designed filter and the original system. Based on the stochastic Lyapunov function, a sufficient condition is developed to guarantee the stochastic stability of the filtering error systems with a given dissipative performance. Two different methods for the existence of desired filter are established. Due to the Finsler’s lemma, the second approach has fewer variables to decide and brings less conservatism than the first one. Finally, an example is provided to demonstrate the correctness and advantage of the proposed approaches.

Event-Triggered Fuzzy Filtering for Nonlinear Dynamic Systems via Reduced-Order Approach

Abstract: This paper is concerned with the problem of generalized $\mathcal {H}_{2}$ reduced-order filter design for continuous Takagi–Sugeno fuzzy systems using an event-triggered scheme. For a continuous Takagi–Sugeno fuzzy dynamic system, a reduced-order filter is designed to transform the original model into a linear lower order one. This filter can also approximate the original system with $\mathcal {H}_{2}$ performance, with a new type of event-triggered scheme used to decrease the communication loads and computation resources within the network. By transforming the filtering problem to a convex optimization one, conditions are presented to design the fuzzy reduced-order filter. Finally, two illustrative examples are used to verify the feasibility and applicability of the proposed design scheme.

Modeling and Stability Analysis of $LCL$ -Type Grid-Connected Inverters: A Comprehensive Overview

Abstract: Due to the advantages of superior harmonics attenuation ability and reduced size, the LCL filter has been widely adopted to interface between the inverter and the grid for improving the quality of injected grid currents. However, the high-order characteristics and various constraints of the LCL filter complicate the filter design. Moreover, the stability of the internal current control loop of the individual inverter is susceptible to the inherent LCL -filter resonance peak. Meanwhile, the overall system stability would be aggravated by the external interactions between the inverter and the weak grid as well as among the paralleled inverters. Both the LCL -filter resonance peak and two types of interaction would cause severely distorted grid currents. Motivated by the existing problems, a comprehensive review on the modeling and stability analysis of the LCL -type grid-connected inverters is conducted in this paper. Concretely, the generalized parameter constraints of the LCL filter are outlined to facilitate the passive components selection, and the magnetic integration techniques of filter inductors are also introduced to reduce the weight and size of filter for increasing the power density of the system. Then, the various damping methods for enhancing the individual internal stability and the relevant application issues are also discussed. Furthermore, the impedance-based method for evaluating the system-level interactive stability is subsequently reviewed, with the emphasis on different modeling methods of inverter output impedance and online impedance measurement techniques. Finally, the future research trends on the modeling and stability analysis of LCL -type grid-connected inverters are also presented.

Particle swarm optimization algorithm to solve the deconvolution problem for rolling element bearing fault diagnosis.

Abstract: Extraction of the fault related impulses from the raw vibration signal is important for rolling element bearing fault diagnosis. Deconvolution techniques, such as minimum entropy deconvolution (MED), MED adjusted (MEDA) and maximum correlated kurtosis deconvolution (MCKD), optimal MED adjusted (OMEDA) and multipoint optimal MED adjusted (MOMEDA), are typical techniques for enhancing the impulse-like component in the fault signal. This paper introduces the particle swarm optimization (PSO) algorithm to solve the filter of deconvolution problem. The proposed approaches solve the filter coefficients of the deconvolution problems by the PSO algorithm, assisted by a generalized spherical coordinate transformation. Compared with MED, MEDA, and OMEDA, the proposed PSO-MED and PSO-OMEDA can effectively overcome the influence of large random impulses and tend to deconvolve a series of periodic impulses rather than a signal impulse. Compared with MCKD and MOMEDA, the proposed PSO-MCKD and PSO-MOMEDA can achieve good performances even when the fault period is inaccurate. The effectiveness of the proposed methods is validated by the simulated signals. The study of experimental bearing fault signal shows that the PSO based deconvolution methods delivered better performance for rolling element bearing fault detection than the traditional deconvolution methods. Additionally, the proposed methods are compared with the following two popular signal processing methods: the ensemble empirical mode decomposition (EEMD) and fast kurtogram, which are used to highlight the improved performance of the proposed methods.

Filter Design for Autoregressive Moving Average Graph Filters

Abstract: In the field of signal processing on graphs, graph filters play a crucial role in processing the spectrum of graph signals. This paper proposes two different strategies for designing autoregressive moving average (ARMA) graph filters on both directed and undirected graphs. The first approach is inspired by Prony's method, which considers a modified error between the modeled and the desired frequency response. The second technique is based on an iterative approach, which finds the filter coefficients by iteratively minimizing the true error (instead of the modified error) between the modeled and the desired frequency response. The performance of the proposed algorithms is evaluated and compared with finite impulse response (FIR) graph filters, on both synthetic and real data. The obtained results show that ARMA filters outperform FIR filters in terms of approximation accuracy and they are suitable for graph signal interpolation, compression, and prediction.

Reliable Filtering of Nonlinear Markovian Jump Systems: The Continuous-Time Case

Abstract: This paper is concerned with the reliable $\boldsymbol {\mathscr {L}_{2}-\mathscr {L}_{\infty} }$ filter design problem for the nonlinear continuous-time Markov jump systems based on Takagi–Sugeno fuzzy model. A stochastic variable is introduced to describe the encountered sensor failures, the value of which is dependent on the considered plant mode based on a hidden Markov process. In practice, generally the information on plant modes is not fully accessible to the reliable filter, which results in the nonsynchronous phenomena between the modes of involved plant and filter, and has a negative effect on the system performance. A hidden Markov model is also adopted to depict such kinds of nonsynchronous phenomena. The filtering error systems are called fuzzy dual hidden Markov jump systems. A sufficient condition, associated to the modes of the plant, sensor failures, and the filter are proposed for the filtering error systems to ensure the stochastic stability and guaranteed $\boldsymbol {\mathscr {L}_{2}-\mathscr {L}_{\infty} }$ performance, based on which the existence condition and explicit design method of a nonsynchronous filter are both given. Finally, two simulation examples illustrate the effectiveness of the proposed approach.

Harmonic Overloading Minimization of Frequency-Dependent Components in Harmonics Polluted Distribution Systems Using Harris Hawks Optimization Algorithm

Abstract: This paper presents a novel approach to optimal planning of a resonance-free C-type harmonic filter to minimize the harmonic overloading level of frequency-dependent components in a non-sinusoidal distribution system. In the studied system, the non-sinusoidal conditions are represented by the utility side's background voltage distortion and the load side's current distortion in addition to the harmonic characteristics of the utility, power cable, distribution transformer, and hybrid linear and nonlinear loads. A constrained optimization problem is formulated to find the optimal filter design that can enhance the power quality performance of the system while complying with the harmonic limits reported in the IEEE Standard 519, filter operation limits reported in the IEEE Standard 18, and other sets of operational ranges to maintain voltage and power factors within their acceptable limits, in addition to diminishing harmonic resonance hazards that may arise due to the filter connection. The problem is solved using a recent swarm intelligence optimization algorithm called the Harris hawks optimization (HHO) algorithm. The results obtained by the conventional methods presented in the literature, namely loss-based and adjusted power factor expressions, are compared with the results obtained by the proposed methodology for validation of the solution. Besides, the problem is solved using other swarm intelligence methods and these methods are compared with the HHO algorithm. The results obtained show the effectiveness of the approach proposed using HHO in finding the minimum power loss and harmonic overloading level of the frequency-dependent components compared to the other optimizers.

Set-Membership Filtering for State-Saturated Systems With Mixed Time-Delays Under Weighted Try-Once-Discard Protocol

Abstract: This brief is concerned with the set-membership filtering problem for a class of time-varying state-saturated systems with mixed time-delays under the communication protocol. Under the weighted try-once-discard (WTOD) protocol, only the sensor node with the largest measurement difference is allowed to access the shared communication network at each transmission instant. The purpose of the problem addressed is to design a set of set-membership filters such that, in the simultaneous presence of mixed time-delays, state saturation, WTOD protocol and bounded noises, the filtering error dynamics is confined to certain ellipsoid regions. A sufficient condition is derived to guarantee the existence of the desired set-membership filters by means of the solutions to a set of recursive linear matrix inequalities. Subsequently, an optimization problem subject to certain inequality constraints is put forward to acquire the minimized ellipsoid in the sense of matrix trace. A simulation example is presented to illustrate the effectiveness of the proposed filter design scheme.

A Constant Switching Frequency Model Predictive Control Without Weighting Factors for T-Type Single-Phase Three-Level Inverters

Abstract: This paper presents a novel model predictive control (MPC) for the T-type single-phase three-level inverters. The cumbersome procedure in tuning of weighting factors is eliminated so that the implementation of the proposed MPC becomes easy. The constant switching frequency is achieved during the control implementation in order to facilitate the filter design. In order to optimize the distribution of the output current harmonics, multiple voltage vectors are employed in each control cycle with their application times setting inversely proportion to the respective cost function. The redundant small voltage vectors are utilized to balance the neutral point voltage of the inverter by directly regulating the upper and lower dc-link capacitors voltages. Finally, the proposed MPC algorithm is experimentally evaluated and compared with other two unfixed switching frequency MPC algorithms in terms of the steady state, transient performance, and parameter sensitivity.

5 GHz Band n79 wideband microacoustic filter using thin lithium niobate membrane

Abstract: Microacoustic resonators made on suspended continuous membranes of LiNbO 3 were recently shown to have very strong coupling and low losses at ≥5 GHz, suitable for high-performance filter design. Employing these simple resonator structures, the authors have designed, fabricated, and measured a 4.7 GHz bandpass ladder-type filter having 1 dB mid-band loss and 600 MHz bandwidth to address the 5G Band n79 requirements. The filter is fabricated on a monolithic substrate using standard i-line optical lithography and standard semiconductor processing methods for membrane release, starting with commercially available ion-sliced wafers having 400 nm thickness crystalline LiNbO 3 layers. The filter is well-matched to a 50 Ω network and does not require external matching elements. Through accurate resonator engineering using our finite element method software filter design environment, the passband is spurious-free, and the filter provides better-than 30 dB rejection to the adjacent WiFi frequencies. This filter demonstrates the performance and scalable technology required for high-volume manufacturing of microacoustic filters >3.5 GHz.

Integral-Based Event-Triggered Fault Detection Filter Design for Unmanned Surface Vehicles

Abstract: This paper is concerned with the event-triggered fault detection filter (FDF) design for an unmanned surface vehicle (USV) under the network environment. A framework of fault detection is established for a USV subject to wave-induced disturbance and actuator failures, in which an FDF is utilized to construct a residual model and an integral-based event generator is introduced to save communication resources. Compared with the traditional instantaneous value based event-triggering scheme and periodic sampling, the proposed event-triggering mechanism can not only reduce bandwidth utilization of the network more significantly, but also get rid of the Zeno phenomenon fundamentally. The event-triggering scheme and the FDF are co-designed. Finally, the efficient performance of the proposed fault detection method based on integral-based event-triggering scheme is illustrated by simulation.

Finite-time filtering for Itô stochastic Markovian jump systems with distributed time-varying delays based on optimisation algorithm

Abstract: This study is concerned with the problem of finite-time filter design for a class of Ito stochastic systems with Markovian switching and distributed time-varying delays. Firstly, a partially mode-dependent filter is designed to accommodate to unreliable network transmission. The attention is focused on deriving sufficient conditions for the filtering error system to ensure the finite-time boundedness and to satisfy a prescribed disturbance attenuation. Then based on stochastic functional theory, the existence of filter is presented by solving existing linear matrix inequalities optimisation problems. Furthermore, the result is extended to the case where the mode information is completely transmitted. Finally, a numerical example is provided to show the effectiveness of the proposed results.

Distributed $H_\infty$ State Estimation Over a Filtering Network With Time-Varying and Switching Topology and Partial Information Exchange

Abstract: This paper is concerned with the distributed ${H_\infty }$ state estimation for a discrete-time target linear system over a filtering network with time-varying and switching topology and partial information exchange. Both filtering network topology switching and partial information exchange between filters are simultaneously considered in the filter design. The topology under consideration evolves not only over time but also by an event switch which is assumed to be subject to a nonhomogeneous Markov chain . The probability transition matrix of the nonhomogeneous Markov chain is time-varying . In the filter information exchange, partial state estimation information and channel noise are simultaneously considered. In order to design such a switching filtering network with partial information exchange, stochastic Markov stability theory is developed. The switching topology-dependent filters are derived to guarantee an optimal ${H_{\infty }}$ disturbance rejection attenuation level for the estimation disagreement of the filtering network. It is shown that the addressed ${H_\infty }$ state estimation problem is turned into a switching topology-dependent optimal problem. The distributed filtering problem with complete information exchanges from its neighbors is also investigated. An illustrative example is given to show the applicability of the obtained results.

Increasing Motion Fidelity in Driving Simulators Using a Fuzzy-Based Washout Filter

Abstract: The motion cueing algorithm is the procedure used to regenerate vehicle motion cues by transforming translational and rotational motions of a simulated vehicle into the simulator motion such that high fidelity motions can be generated through a washout filter. Classical washout filters are widely being used in different motion simulators because of their low computational load, simplicity, and functionality. However, they have a number of disadvantages that make them unreliable in some cases. One of the main disadvantages is its parameter selecting procedure, which is based on trial-and-error and the worst case driving scenario. In addition, the washout filter parameters are kept constant during different driving scenarios, causing inflexibility of the filter design. This causes conservative platform workspace usage and as a result false motion cues. Furthermore, the mathematical model of human motion sensation plays no role in designing of the classical washout filter. Ignorance of the online information about physical limitations of the platform and the simulator driver motion sensation factors are other main drawbacks of classical washout filters. The aim of this research is to provide a fuzzy logic-based washout filter that considers the motion sensation error between the real vehicle and simulator drivers as well as the distance of the simulator platform from its physical boundaries to correct false motion cues and decrease human sensation errors. The proposed fuzzy-based washout filter also seeks to increase motion fidelity and enable the simulator to use the platform workspace more efficiently. The simulation results show the efficiency of the proposed fuzzy-based washout filter in reducing the human sensation error and enhancing efficiency of the simulator platform workspace usage.

Active EMI Filter Design With a Modified LCL-LC Filter for Single-Phase Grid-Connected Inverter in Vehicle-to-Grid Application

Abstract: More and more attention has been gained in the vehicle-to-grid (V2G) system due to its attractive features, such as transferring bidirectional power between electric vehicles and grid, regulating grid voltage/frequency. This paper develops the active electromagnetic interference (EMI) filter (AEF) with significant system volume and weight reduction compared to the traditional passive EMI filter (PEF). While the high-frequency parasitic parameters of the AEF and the restricted loop gain result in limited AEF EMI noise attenuation in the high-frequency range. Thus, an additional passive EMI filter is commonly needed to suppress the EMI noise. For avoiding external passive filter components, the active EMI filter is designed and integrated with a modified LCL-LC filter as a hybrid filter to guarantee EMI noise attenuation. The modified LCL-LC filter can maintain satisfactory harmonic suppression with a higher EMI reduction ability compared to other types of harmonic filters. Based on a single-phase grid-connected inverter, the total harmonic distortion (THD) and the conducted EMI measurement results are compared with the results of traditional LCL, LLCL, LCL-LC filters. The effectiveness of the proposed circuit is verified by simulation and experimental results. The proposed circuit successfully improves the filter design of the inverter in the suppression of harmonics and EMI noise.

A High-Precision Time Skew Estimation and Correction Technique for Time-Interleaved ADCs

Abstract: This paper presents an all-digital background calibration technique for the time skew mismatch in time-interleaved ADCs (TIADCs). The technique jointly estimates all of the time skew values by processing the outputs of a bank of correlators. A low-complexity sampling sequence intervention technique, suitable for successive approximation register (SAR) ADC architectures, is proposed to overcome the limitations associated with blind estimation. A two-stage digital correction mechanism based on the Taylor series is proposed to satisfy the target high-precision correction. A quantitative study is performed regarding the requirements imposed on the digital correction circuit in order to satisfy the target performance and yield, and a corresponding filter design method is proposed, which is tailored to meet these requirements. Mitchell’s logarithmic multiplier is adopted for the implementation of the principal multipliers in both the estimation and correction mechanisms, leading to a 25% area and power reduction in the estimation circuit. The proposed calibration is synthesized using a TSMC 28-nm HPL process targeting a 2.4-GHz sampling frequency for an eight-sub-ADC system. The calibration block occupies 0.03 mm2 and consumes 11 mW. The algorithm maintains the SNDR above 65 dB for a sinusoidal input within the target bandwidth.

Tunable Liquid Crystal Filter in Nonradiative Dielectric Waveguide Technology at 60 GHz

Abstract: This letter presents a continuously tunable nonradiative dielectric waveguide three-pole bandpass filter based on liquid crystal (LC) technology at 60 GHz. LC is used as tunable material to obtain a continuous tunable center frequency. For the LC’s orientation, an electrode network with parasitic mode suppressive structure is realized. The measurements are performed with magnetic and electric biasing to verify the performance of the filter design. A maximum fractional bandwidth of 1% and a tunability of 2.5% were measured with electrical biasing. Furthermore, the filter’s insertion loss is between 4.9 and 6.2 dB over the tuning range. The extracted unloaded quality factor for electrical biasing ranges from 96 to 141.

A Fast and Precise Method for Modeling EMI Source in Two-Level Three-Phase Converter

Abstract: Currently, frequency-domain electromagnetic interference (EMI) modeling has become the most commonly used method for EMI prediction and filter design. However, because of the unpredictability of the ringing effect in the present frequency-domain EMI modeling methods, the precision of the predicted EMI interference source is notably limited, which results in the inaccuracy of the system level EMI prediction. In order to achieve an accurate EMI prediction, this study presents a systematical study on the interference source of a 10 kVA three-phase bridge converter. The precise mathematical models of the common-mode and differential-mode interference source are constructed. Then, the characteristics of the multiple ringing effect, which is an important part of the interference source, are intensively analyzed. Based on the investigation of interference source, an improved interference source synthesis method is proposed. It can quickly and precisely predict the CM and DM interference source with multiple ringing effect, which can be used to achieve accurate frequency domain EMI prediction. Moreover, the multiple ringing effect is extended for IGBT parasitic parameter measuring. Instead of complicated device-level measurement, the grounding capacitance and junction capacitance can be effectively extracted through the ringing frequency measurement.

Decentralized Dissipative Filtering for Delayed Nonlinear Interconnected Systems Based on T–S Fuzzy Model

Abstract: This paper focuses on the problem of dissipative filtering for nonlinear interconnected systems with interval time-varying delays The considered nonlinear interconnected system is modeled by Takagi–Sugeno fuzzy rules By constructing the delay-dependent Lyapunov–Krasovskii functional and using new integral inequality, the delay-dependent condition is established to ensure that the derived closed-loop system is asymptotically stable with strict $(Q, S,R)-\alpha -$ dissipativity In addition, a suitable filter is designed by solving a set of linear matrix inequalities The presented method can provide better performance than the existing ones for the case of $\mathcal {H}_\infty$ filtering A simulation example is given to demonstrate the validity of the developed filter design technique