Time-varying Filter Research Articles

Autoregressive Based Vocal Tract Shape Modelling of Vowels in Speech Processing

In the present research article, which concentrates on the estimation of the vocal tract shape for the five vowels of Indian English in the current growing interest in the field of speech processing, with practical limitation of the data that can be collected and analysed. Speakers without the degree of control on articulators cannot produce the desired data. Here, we aim to design an approximate phonatory model by improving the present approach for acoustic calculation with a fully aerodynamic simulation, explicitly accounting for the propagation of sound along the tract, generating patterns of movement of simulated vocal tract articulators, and specifying the temporal relations among dynamically defined gestures that lead to a time-varying vocal tract filter function, and an acoustic waveform. The principle of phonetic distinctiveness and formants frequency spread is one of the crucial parameters that are measured and compared on intra, inter-speaker in actual speaker identification & forensic applications.

Adaptive event-triggered filtering for semi-Markov jump systems under communication constraints

This paper investigates the problem of adaptive event-triggered filtering for a class of semi-Markov jump systems. A more general scenario of semi-Markov jump systems under communication constraints is explored, where the output signal occurs with saturation nonlinearity, packet loss, and quantization. The saturation nonlinearity and packet dropout are described by two random variables subject to Bernoulli distribution. The mode-dependent adaptive event-triggered mechanism is designed by combining the system mode information. Based on the output error, the trigger threshold adaptive law is designed to make the threshold change dynamically. The mode-dependent time-varying filter is constructed and sufficient conditions for the desired filter with H∞ performance are obtained. Finally, a single-link robot arm system verifies the effectiveness of the proposed filter design method, and the adaptive event-triggered protocol can effectively reduce the number of data transmissions.

The Performance of a Time-Varying Filter Time Under Stable Conditions over Mountainous Terrain

Eddy-covariance data from five stations in the Inn Valley, Austria, are analyzed for stable conditions to determine the gap scale that separates turbulent from large-scale, non-turbulent motions. The gap scale is identified from (co)spectra calculated from different variables using both Fourier analysis and multi-resolution flux decomposition. A correlation is found between the gap scale and the mean wind speed and stability parameter z/L that is used to determine a time-varying filter time, whose performance in separating turbulent and non-turbulent motions is compared to the performance of constant filter times between 0.5 and 30 min. The impact of applying different filter times on the turbulence statistics depends on the parameter and location, with a comparatively smaller impact on the variance of the vertical wind component than on the horizontal components and the turbulent fluxes. Results indicate that a time-varying filter time based on a multi-variable fit taking both mean wind speed and stability into account and a constant filter time of 2–3 min perform best in that they remove most of the non-turbulent motions while at the same time capturing most of the turbulence. For the studied sites and conditions, a time-varying filter time does not outperform a well chosen constant filter time because of relatively small variations in the filter time predicted by the correlation with mean flow parameters.

Multivariate time-varying Kalman filter approach for cycle-based maximum queue length estimation

Real-time queue length is a crucial information, increasingly needed for signal operation and signal optimization purposes. This paper proposes a novel multivariate time-varying Kalman filter approach to estimate the cycle-based maximum queue lengths in real-time by only using high-resolution vehicle loop detector data and signal timing data. The modeling framework builds on state changing points that can be identified at detector site and shockwave theory that models the propagation of traffic states in the space–time domain. The times at which traffic states change at the detector site, known as break points (e.g., free flow to jam condition, jam to saturation condition, etc.) are identified using high resolution detector data and the end of the queue is estimated using linear models that build on the shockwave theory. Nevertheless, this deterministic approach, which is previously proposed in the literature, does not account for measurements errors associated with traffic state changing points or the assumptions in the shockwave theory. Therefore, we propose a multivariate time-varying Kalman filter approach to produce a robust estimate for the cycle-based maximum queue length in both undersaturated and oversaturated conditions. Further, the developed algorithm can estimate the residual queue length and inform its occurrence in case of oversaturation. The proposed methodology can easily be adapted in practice, as it relies solely on measurements from a single loop detector that may be located close to the stop line.

Passivity Fractional-Order Sliding-Mode Control of Grid-Connected Converter With LCL Filter

Grid-connected converter with LCL-filter (LCL-GCC) plays an important role in renewable power generation systems. However, existing control techniques face several challenges in practice (e.g., difficulty in parameter design, slow dynamic response, and poor robustness under slowly time-varying filter parameter, dead-time of power devices and external disturbances). To solve these difficulties, a passivity-based fractional-order sliding-mode control (PBC-FOSMC) hybrid controller, which combines the merits of PBC and FOSMC, is proposed, where the inputs of the passive controller are the outputs of the FOSMC controller. First, a passive current controller is designed based on the Euler-Lagrange (EL) model established by damping injection according to the passivity-based control (PBC) theory, which can make the system automatically converge to meet the energy dissipation law. Secondly, a FOSMC controller is designed to further enhance the system robustness to counter disturbances and compensate the reference current accuracy of PBC. The introduced fractional order can successfully suppress the undesired chattering due to the switching behaviors in conventional SMC. Thirdly, the hybrid PBC-FOSMC controller is derived and system stability is analyzed. Finally, experimental results under 10-kW prototype validate the excellent performance of the proposed method in terms of robustness, dynamic performance, strong perturbation rejection ability, etc., and the desired control targets are achieved.

Improvements to a Method to Simulate Non-Stationary Wind Noise in Vehicles

As the vehicle and wind speeds and directions change, the unsteady flow creates non-stationary wind noise. To investigate people's perceptions of non-stationary wind noise, a method to simulate the non-stationary wind noise is needed. Previously, a method was developed that used stationary recordings taken at several speeds and directions to create a set of sound pressure level predictions in each one-third octave band that are a function of wind speed and direction. These functions are used to create time-varying filters based on provided wind profiles. A reference wind noise measurement is then filtered to produce the sounds. A drawback of this method is that many stationary wind condition measurements are needed to form accurate sound pressure level functions, which can be time consuming. A method requiring fewer measurements was investigated. At each yaw angle one non-stationary noise measurement and one stationary reference measurement are used to estimate the relationship between sound power and wind speed. The accuracy of the estimation was validated by comparing wind noise simulations with wind tunnel measurements, A further improvement is to use partially correlated white noise signals to simulate binaural sounds that have a similar coherence structure between the left and right ear sounds to that observed in binaural measurements in the vehicle.

An adaptive autoregressive pre-whitener for speech and acoustic signals based on parametric NMF

A common assumption in many speech and acoustic processing methods is that the noise is white and Gaussian (WGN). Although making this assumption results in simple and computationally attractive methods, the assumption is often too simple and crude in many applications. In this paper, we introduce a general purpose and online pre-whitener which can be used as a pre-processor with methods based on the WGN assumption, improving their reliability and performance in applications with colored noise. The pre-whitener is a time-varying filter whose coefficients are found using a parametric non-negative matrix factorization (NMF), based on autoregressive (AR) mixture modeling of both the noise component and the signal component constituting the noisy signal. Compared to other types of pre-whiteners, we show that the proposed pre-whitener has the best performance, especially in applications with non-stationary noise. We also perform a large number of experiments to quantify the benefits of using a pre-whitener as a pre-processor for methods based on the WGN-assumption. The applications of interest were pitch estimation and time-of-arrival (TOA) estimation, where the WGN assumption is very popular.

Event-triggered H∞ robust filtering for nonlinear semi-Markov switching systems

This paper studies the filtering problem for a class of discrete-time semi-Markov switching repeated scalar nonlinear systems with an event-triggered scheme. Considering the limitation of bandwidth, the mode-dependent event-triggered mechanism is introduced to determine whether the currently sampled sensor data is transmitted to the filter, in which the parameters of the event generator depend on the system mode. By means of a constructed time-varying Lyapunov function, the two stages of the systems jump instant and mode residence are analysed, and a linear matrix inequality technique is used to ensure the mean-square stability and performance of the filter error system. Accordingly, the co-design method of the mode-dependent time-varying filter and the event-triggered mechanism is derived for semi-Markov switching systems. Finally, a numerical simulation is given to illustrate the effectiveness and feasibility of the method proposed in this paper.

Finite-horizon distributed set-membership filtering with dynamical bias and DoS attacks under binary encoding schemes

In this work, we cope with the design issue of finite-horizon distributed set-membership filters concerning a set of time-varying systems (TVSs) over sensor networks (SNs). The system to be discussed is suffering from dynamical bias and unknown but bounded noise. The binary encoding schemes (BESs) are utilized in the data transmission through SNs from neighbors to the intelligent sensor nodes, which exhibit robustness to some degree due to usage of the binary data. Both random bit errors caused by channel noises and denial of service (DoS) attacks reflecting network vulnerability are considered that may arise and lead to signal distortion in the transmission process of the binary bit string. In the framework of adopting BESs, our aim is to realize the distributed set-membership filtering for TVSs over SNs limited by dynamical bias and DoS attacks. Sufficient conditions, relying on the solvability of a class of recursive linear matrix inequalities, are obtained for the presence of the expected time-varying distributed set-membership filters. Furthermore, an additional optimization issue is constructed to find the minimal ellipsoids (on the basis of matrix trace) to obtain the best filtering performance. Eventually, a numerical simulation instance is provided to verify the availability of the presented design strategy of finite-horizon distributed set-membership filters.

Distributed Filtering for Semi-Markov-Type Sensor Networks With Hybrid Sojourn-Time Distributions—A Nonmonotonic Approach

This article examines the distributed filtering problem for a general class of filtering systems consisting of distributed time-delayed plant and filtering networks with semi-Markov-type topology switching (SMTTS). The SMTTS implies the topology sojourn time can be a hybrid function of different types of probabilistic distributions, typically, binomial distribution used to model unreliable communication links between the filtering nodes and Weibull distribution employed to depict the cumulative abrasion failure. First, by properly constructing a sojourn-time-dependent Lyapunov-Krasovski function (STDLKF), both time-varying topology-dependent filter and topology-dependent filter are designed. Second, a novel nonmonotonic approach with less design conservatism is developed by relaxing the monotonic requirement of STDLKF within each topology sojourn time. Moreover, an algorithm with less computational effort is proposed to generate a semi-Markov chain from a given Markov renewal chain. Simulation examples, including a microgrid islanded system, are presented to testify the generality and elucidate the practical potential of the nonmonotonic approach.

Sound zone control with fixed acoustic contrast and simultaneous tracking of acoustic transfer functions

As environmental changes are inevitable, online algorithms that can track the variation of acoustic transfer functions are preferred in sound zone control (SZC) systems. In this paper, it is found that for the time domain SZC, the nonuniqueness problem caused by the coupling in multichannel transfer function modeling can be solved with time-varying control filters. To keep a stable acoustic contrast (AC) performance irrespective of environmental changes and also avoid the extreme distortion introduced by maximizing the AC in the time domain, the objective of maintaining a desired level of AC is used and an adaptive control algorithm is developed. The simulations validate that the proposed method tracks the environmental changes and adapts the control filters to give a stable AC performance.

Mechanical varying non-stationary signal separation method based on instantaneous frequency estimation

Mechanical equipment often works on variable speed condition, its corresponding vibration signal presents multi-component, modulation coupling with fast time-varying instantaneous frequency (IF), how to effectively compute IF and realize fasting varying non-stationary signal decoupling separation plays an important role in mechanical system fault diagnosis. In this paper, a sparse representation method called multi-scale chirp sparse representation (MSCSR) is introduced to identify, extract, and trend IF for achieving a highly concentrated time-frequency energy. Simulation demonstrates that the proposed method performs better than traditional IF estimation method. Furthermore, an adaptive time-varying filter is constructed using the extracted instantaneous frequency to decouple non-stationary fast signal. Ultimately, by rapid instantaneous frequency fluctuation experiment, the effectiveness of proposed method for fast strong time-varying signal is validated, it can effectively extract rapid oscillation instantaneous frequency, and the error is less than 10%.

An enhanced monthly runoff time series prediction using extreme learning machine optimized by salp swarm algorithm based on time varying filtering based empirical mode decomposition

Reliable runoff prediction plays a significant role in reservoir scheduling, water resources management, and efficient utilization of water resources. To effectively enhance the prediction accuracy of monthly runoff series, a hybrid prediction model (TVF-EMD-SSA-ELM) combining time varying filtering (TVF) based empirical mode decomposition (EMD), salp swarm algorithm (SSA) and extreme learning machine (ELM) is proposed. Firstly, the monthly runoff series is decomposed into several sub-series using TVF-EMD. Secondly, SSA is used to optimize the input weights and hidden layer biases of the selected ELM model. Finally, the prediction results are generated by summing and reconstructing each sub-series based on the SSA optimized ELM model. This hybrid model is applied to the monthly runoff prediction of Manwan hydropower, Hongjiadu hydropower, and Yingluoxia hydrological station, and compared with back propagation (BP), ELM, SSA-ELM, PSO-ELM, GSA-ELM, TVF-EMD-ELM, EMD-SSA-ELM, extreme-point symmetric mode decomposition (ESMD)-SSA-ELM, wavelet decomposition (WD)-SSA-ELM and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN)-SSA-ELM models. The prediction performance of various models is reflected by four evaluation indicators (R, NSEC, NRMSE, MAPE). Results reveal that the prediction effect of the ELM model is better than that of BP, the optimization accuracy of SSA is better than those of particle swarm optimization (PSO) and gravitational search algorithm (GSA), and the prediction accuracy of the hybrid TVF-EMD and SSA is better than that of only TVF-EMD or SSA. TVF-EMD-SSA-ELM model has the highest prediction accuracy. When compared with the single ELM model, it’s NRMSE and MAPE at Manwan hydropower decrease by 84.4% and 72.38%, those of Hongjiadu hydropower decrease by 85.21% and 78.38%, and those of Yingluoxi hydrological station decrease by 68.42% and 39.51%, respectively. R and NSEC of the three sites are close to 1. Therefore, the proposed model provides a new method for the prediction of monthly runoff, and the results can provide a reference for the prediction of monthly runoff in the study area.

Discrete-time Kalman filter for heave motion estimation

This paper addresses the problem of estimating the heave motion of a platform using biased measurements of an accelerometer. We develop a general framework wherein convenient and well-known properties of trigonometric functions are exploited to devise a linear system whose state encompasses implicit representations of wave amplitudes and phase shifts, as well as a constant sensor bias. The observability of the system is analyzed, followed naturally by the implementation of a discrete-time linear time-varying Kalman filter with global asymptotic stability guarantees. Our proposed methodology is validated with realistic numerical examples, including an accurate representation of a continuous wave spectrum within an ocean context.

A time-varying Kalman filter for low-acceleration attitude estimation

This work shows an attitude estimator (AE) based on a time-varying Kalman filter (TVKF) and adapted to those cases where a low-acceleration assumption can be applied. This filter is an extended version of a previously published time-varying Kalman filter attitude estimator (TVKAE). A comparative analysis of the accuracies of those two estimators is provided. The efficiencies of both filters are also compared with those of other published AEs. The results show that the new AE achieves the best overall performance, followed by the original one.

Time-varying filtering for nonstationary signal analysis of rotating machinery: Principle and applications

Rotating machinery vibration signals are composed of multiple time-varying frequency components under nonstationary conditions, and they usually overlap in the frequency domain. Effective extraction of these time-varying components is vital for rotating machinery fault diagnosis. This paper proposes a time-varying filtering (TVF) method for extraction of time-varying components. Its upper and lower cutoff frequencies are time-varying and independent. The TVF uses locally designed bandpass filters to achieve local filtering in the time domain, and exploits filtering with phase correction (FPC) and local filter lengths to ensure the phase and amplitude consistency of filtered signals. It cannot only extract time-varying mono-components but also separate time-varying frequency bands from highly nonstationary signals, for further time–frequency analysis: (1) fine time–frequency representations based on mono-components extraction, and (2) time-varying demodulation of time-varying frequency band of interests. The proposed method is illustrated and validated through numerical simulation, and signal analyses of typical rotating machines including a hydraulic turbine rotor, an aircraft engine rolling bearing, a multistage planetary gearbox, and a fixed shaft gearbox.

Joint actuator fault estimation and localization under Round-Robin protocol for wheeled mobile robot

In this paper, the joint actuator fault estimation (AFE) and the wheeled mobile robot (WMR) localization under the Round-Robin protocol (RRP) problems are concerned. In order to complete the joint AFE and the WMR localization, a nominal joint system is constructed, which consists of the drive subsystem and WMR localization subsystem. In the drive subsystem of the WMR, the DC motor is used as an actuator to drive the WMR. When the faults occur, the performance of the actuator will be degraded, and the mobility of the WMR will be affected. In order to maintain a satisfactory mobility of the WMR, the faults need to be estimated timely such that some appropriate decisions or remedies can be made. In the WMR localization subsystem, for saving the network resources, the RRP is introduced to schedule the transmission of sensor measurements used for the WMR localization. The purpose of this paper is, by designing a time-varying filter for the constructed joint nominal system, to ensure the filtering error to meet the given [Formula: see text] performance requirement, such that the joint AFE and the WMR localization can be achieved simultaneously. Specifically, the sufficient condition is derived first and then the desired filter gain is designed by the recursive linear matrix inequality technology. Finally, a simulation experiment is conducted to certify the usefulness of the proposed algorithm.

Real-time sound synthesis of pass-by noise: comparison of spherical harmonics and time-varying filters

This paper proposes and compares two sound synthesis techniques to render a moving source for a fixed receiver position based on indoor pass-by noise measurements. The approaches are based on the time-varying infinite impulse response (IIR) filtering and spherical harmonics (SH) representation. The central contribution of the work is a framework for realistic moving source sound synthesis based on transfer functions measured using static far-field microphone arrays. While the SHs require a circular microphone array and a free-field propagation (delay, geometric spread), the IIR filtering relies on far-field microphones that correspond to the propagation path of the moving source. Both frameworks aim to provide accurate sound pressure levels in the far-field that comply with standards. Moreover, the frameworks can be extended to additional sources and filters (e.g. sound barriers) to create different moving source scenarios by removing the room size constraint. The results of the two sound synthesis approaches are preliminary evaluated and compared on a vehicle pass-by noise dataset and it is shown that both approaches are capable of accurately and efficiently synthesize a moving source.

Autonomous Underwater Vehicle Positioning Control - a Velocity Form LPV-MPC Approach*

In this work, the positioning control of an autonomous underwater vehicle (AUV) is considered for docking operations in the presence of varying tidal currents. The AUV model is described by a dynamic model and a kinetic model, both are linear parameter varying (LPV). A velocity form LPV model predictive control (LPV-MPC) scheme is proposed, in which the AUV dynamic model is used for the states and the kinetic model is used for the output. The interdependence of AUV kinematic model and dynamic model is exploited to avoid increased state dimension. The complete velocity form controller design enables the cancellation of disturbance effects through the use of the AUV's velocity vector increment for predicting the future evolution of the system. Compared to the original predictive control for the Naminow-D AUV that uses a time-varying Kalman filter for state estimation to approximate disturbances, the proposed algorithm does not require an estimator to eliminate unknown current disturbance, therefore simplifies design and implementation. Simulation studies show the merit of the proposed controller over the original Naminow-D predictive controller especially in terms of improved transient response and reduced sensitivity to time-varying external disturbances.

Data-driven adaptive chirp mode decomposition with application to machine fault diagnosis under non-stationary conditions

Rotating machineries play a significant role in industrial application and fault diagnosis is an important technology to ensure their safe operation. However, the complicated operating environment makes the condition monitoring signals usually display nonlinear and non-stationary characteristics, which brings severe challenges to fault diagnosis. Although adaptive chirp mode decomposition (ACMD) shows good adaptability and high time-frequency resolution for non-stationary signals, it depends on an instantaneous frequency (IF) initialization based on Hilbert transform, which limits its practical applications. In this paper, a fully data-driven adaptive chirp mode decomposition (DD-ACMD) is proposed to address the issue. Firstly, the high-frequency modes of the signal are enhanced by derivative operation, and then the IF of the highest-frequency mode is preliminarily estimated based on a normalization operator. Next, an iterative time-varying filtering method based on a demodulation technique is proposed to reduce the influence of noise and thus obtain good estimates of initial IFs for the ACMD. In addition, a time-varying low-pass filter is introduced into the recursive framework of mode extraction to further improve the noise robustness of the whole algorithm. The DD-ACMD has both high adaptability and good noise robustness, and can even separate non-stationary signals with very close modes. The effectiveness of the DD-ACMD is validated by both simulations and real-life applications to machine fault diagnosis.