Recursive Filtering Approach Research Articles

A multi-scale second-order autoregressive recursive filter approach for the sea ice concentration analysis

A multi-scale second-order autoregressive recursive filter approach for the sea ice concentration analysis

Effect of Soil–Structure Interaction on Inelastic Displacement Ratios of Bridge Structures Subjected to Pulse-Like Ground Motions

This study aimed to investigate the effect of soil–structure interaction (SSI) on the inelastic displacement ratio of bridge structures subjected to near-fault pulse-like earthquake ground motions. SSI was modeled using a discrete-time recursive filter approach and frequency-dependent foundation–soil impedance functions. A total of 105 near-fault earthquake ground motion records were used for the analysis. The study also compared the soil–foundation–superstructure (SFS) system with a fixed-base system to explore the effects of spectral period normalization, strength reduction factor, earthquake magnitude, site-to-source distance, and peak ground velocity on the inelastic displacement ratio. The findings indicate that both the SFS and fixed-base systems have significant inelastic displacement demand under near-fault earthquakes, with values that can be 2–3 times higher than those predicted by ASCE-41-13, particularly in the short period range. Furthermore, as the structural period approaches the earthquake pulse period, the SFS system generally shows larger inelastic displacement ratios compared to the fixed-base system. The analytical results provide insight into the factors affecting the inelastic displacement ratio for near-fault pulse-like earthquakes.

Energy-Based Evaluation on Soil–Structure-Interaction-Related Damping of Inelastic Bridge Pier Structure Subjected to Pulse-like Velocity Ground Motion

This study proposed a procedure of using the energy method to evaluate the SSI-related damping effect when the soil–structure interaction (SSI) was considered in a bridge pier system, which can thus be deemed a soil–foundation–superstructure (SFS) inelastic system. Firstly, the SSI is implemented by adopting a discrete-time recursive filter approach as well as frequency-dependent foundation–soil impedance functions to solve for the external soil forces exerted onto the foundation. Then, by integrating such external soil forces into the motion equations of the SSI-based SFS system, the energy equations can be formulated during the ground motions. To demonstrate the proposed procedure, an implementation study involving a bridge pier was carried out, considering two earthquake recordings. The resultant energy quantities and SSI-related damping ratio shed light on how the aspects of earthquake characteristics affected the energy dissipation mechanism of the bridge pier SSI-based SFS system. This proposed procedure renders a promising solution for quantifying the soil role in the seismic energy dissipation of arbitrary single- and multiple-degree-of-freedom systems considering the SSI effect. The results obtained show that the SSI effect was suppressed when the SFS system underwent near-fault earthquakes, which illustrated that the stiffness and damping contribution from the soil was not pronounced. Furthermore, near-fault earthquakes with large incremental velocities may lead to a low SSI-related damping ratio (SSIDR).

A Hydrograph-Based Approach to Improve Satellite-Derived Snow Water Equivalent at the Watershed Scale

For the past few decades, remote sensing has been a valuable tool for deriving global information on snow water equivalent (SWE), where products derived from space-borne passive microwave radiometers are favoured as they respond to snow depth, an important component of SWE. GlobSnow, a novel SWE product, has increased the accuracy of global-scale SWE estimates by combining remotely sensed radiometric data with other physiographic characteristics, such as snow depth, as quantified by climatic stations. However, research has demonstrated that passive microwaves algorithms tend to underestimate SWE for deep snowpack. Approaches were proposed to correct for such underestimation; however, they are computer intensive and complex to implement at the watershed scale. In this study, SWEmax information from the near real time 5-km GlobSnow product, provided by Copernicus and the European Space Agency (ESA) and GlobSnow product at 25 km resolution were corrected using a simple bias correction approach for watershed scale applications. This method, referred to as the Watershed Scale Correction (WSC) approach, estimates the bias based on the direct runoff that occurs during the spring melt season. Direct runoff is estimated on the one hand from SWEmax information as main input. Infiltration is also considered in computing direct runoff. An independent estimation of direct runoff from gauged stations is also performed. Discrepancy between these estimates allows for estimating the bias correction factor. This approach is advantageous as it exploits data that commonly exists i.e., flow at gauged stations and remotely sensed/reanalysis data such as snow cover and precipitation. The WSC approach was applied to watersheds located in Eastern Canada. It was found that the average bias moved from 33.5% with existing GlobSnow product to 18% with the corrected product, using the recommended recursive filter coefficient β of 0.925 for baseflow separation. Results show the usefulness of integrating direct runoff for bias correction of existing GlobSnow product at the watershed scale. In addition, potential benefits are offered using the recursive filter approach for baseflow separation of watersheds with limited in situ SWE measurements, to further reduce overall uncertainties and bias. The WSC approach should be appealing for poorly monitored watersheds where SWE measurements are critical for hydropower production and where snowmelt can pose serious flood-related damages.

A multi-scale high-order recursive filter approach for the sea ice concentration analysis

A multi-scale high-order recursive filter approach for the sea ice concentration analysis

State-of-Charge Estimation of Lithium-Ion Batteries Subject to Random Sensor Data Unavailability: A Recursive Filtering Approach

In this article, the estimation problem of the state of charge (SOC) of Lithium-ion batteries is investigated. In order to truly reflect the unreliability of the sensor measured data, the data missing phenomenon with respect to the sensor measurement (e.g., the terminal voltage) is taken into account for the addressed estimation issue. By introducing a stochastic variable obeying the Bernoulli distribution with a known probability, the random occurrence of the sensor measurement unavailability is well characterized. The second-order resistor–capacitor equivalent circuit model, where the model parameters are identified by the recursive least-squares method, is developed to govern the dynamical behaviors of a Lithium-ion battery. A data-unavailability-resistant nonlinear recursive filtering algorithm is proposed to estimate the real SOC in an unreliable industrial environment. An upper bound of the filtering error covariance is obtained, which is further minimized at each sampling instant. In addition, the filter gain is recursively parameterized by solving an optimization problem with respect to two coupled recursive Riccati-like equations, thereby being suitable for the online implementation. Finally, extensive experiments are conducted to demonstrate the validity of the proposed filtering approach.

Recursive filtering for stochastic parameter systems with measurement quantizations and packet disorders

In this paper, the recursive filtering problem is put forward for stochastic parameter systems subject to quantization effects and packet disorders. Before entering communication networks, measurement outputs are quantized by logarithmic quantizers. The packet disorders result from transmission delays which are provoked by communication constraints and occur randomly in the sensor-to-filter channel. In case of measurement quantizations and packet disorders, the objective of this paper is to devise a novel recursive filter approach that is capable of 1) guaranteeing desired upper bounds on the resultant filtering error covariances; and 2) minimizing such upper bounds by acquiring appropriate filter gains. Furthermore, sufficient conditions are established to ensure the mean-square boundedness of filtering errors by means of stochastic analysis techniques. At last, simulations are given to validate the applicability of our designed approach.

Full Information Estimation for Time-Varying Systems Subject to Round-Robin Scheduling: A Recursive Filter Approach

The full information estimation (FIE) problem is addressed for discrete time-varying systems (TVSs) subject to the effects of a round-Robin (RR) protocol. A shared communication network is adopted for data transmissions between sensor nodes and the state estimator. In order to avoid data collisions in signal transmission, only one sensor node could have access to the network and communicate with the state estimator per time instant. The so-called RR protocol, which is also known as the token ring protocol, is employed to orchestrate the access sequence of sensor nodes, under which the chosen sensor node communicating with the state estimator could be modeled by a periodic function. A novel recursive FIE scheme is developed by defining a modified cost function and using a so-called “backward-propagation-constraints.” The modified cost function represents a special global estimation performance. The solution of the proposed FIE scheme is achieved by solving a minimization problem. Then, the recursive manner of such a solution is studied for the purpose of online applications. For the purpose of ensuring the estimation performance, sufficient conditions are obtained to derive the upper bound of the norm of the state estimation error (SEE). Finally, two illustrative examples are proposed to demonstrate the effectiveness of the developed estimation algorithm.

A processing system to monitor Greenland outlet glacier velocity variations at decadal and seasonal time scales utilizing the Landsat imagery

In this paper we present a monitoring system for area-wide flow-velocity fields of the outlet glaciers of the Greenland Ice Sheet (GrIS) utilizing the freely available Landsat archive. In order to process this large amount of satellite images an almost automatic monitoring system was developed. With the Global Digital Elevation Map V2 (GDEM-V2) of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) an improved orthorectification was applied which, in combination with a destriping correction of the Landsat 7 products with Scan Line Corrector (SLC) failure, leads to more precise flow-velocity information. In addition, outliers were removed using an adaptive, recursive filter approach. For the time span 1972–2012 more than 100,000 flow-velocity fields were derived from over 16,000 optical multi-sensoral Landsat scenes (Landsat 1 to Landsat 7) allowing the determination of the long-term flow-velocity trend. Moreover, the high temporal resolution facilitates the analysis of seasonal flow-velocity variations of numerous outlet glaciers. For many of the major outlet glaciers the results show an acceleration pattern that is consistent with most of the previously published analyses. However, the flow-velocity changes do not have a uniform pattern in their temporal and spatial distribution. This study provides both a high temporal densification and an extension of existing flow-velocity information of the GrIS margin. Finally, this processing chain in its generality allows for the determination of various types of surface displacements and can be applied to other glacier regions.

The recursive combination filter approach of pre-processing for the estimation of standard deviation of RR series.

Variation in the interval between the R-R peaks of the electrocardiogram represents the modulation of the cardiac oscillations by the autonomic nervous system. This variation is contaminated by anomalous signals called ectopic beats, artefacts or noise which mask the true behaviour of heart rate variability. In this paper, we have proposed a combination filter of recursive impulse rejection filter and recursive 20% filter, with recursive application and preference of replacement over removal of abnormal beats to improve the pre-processing of the inter-beat intervals. We have tested this novel recursive combinational method with median method replacement to estimate the standard deviation of normal to normal (SDNN) beat intervals of congestive heart failure (CHF) and normal sinus rhythm subjects. This work discusses the improvement in pre-processing over single use of impulse rejection filter and removal of abnormal beats for heart rate variability for the estimation of SDNN and Poncaré plot descriptors (SD1, SD2, and SD1/SD2) in detail. We have found the 22 ms value of SDNN and 36 ms value of SD2 descriptor of Poincaré plot as clinical indicators in discriminating the normal cases from CHF cases. The pre-processing is also useful in calculation of Lyapunov exponent which is a nonlinear index as Lyapunov exponents calculated after proposed pre-processing modified in a way that it start following the notion of less complex behaviour of diseased states.

Dynamic State Estimation of Power Systems With Quantization Effects: A Recursive Filter Approach.

In this paper, a recursive filter algorithm is developed to deal with the state estimation problem for power systems with quantized nonlinear measurements. The measurements from both the remote terminal units and the phasor measurement unit are subject to quantizations described by a logarithmic quantizer. Attention is focused on the design of a recursive filter such that, in the simultaneous presence of nonlinear measurements and quantization effects, an upper bound for the estimation error covariance is guaranteed and subsequently minimized. Instead of using the traditional approximation methods in nonlinear estimation that simply ignore the linearization errors, we treat both the linearization and quantization errors as norm-bounded uncertainties in the algorithm development so as to improve the performance of the estimator. For the power system with such kind of introduced uncertainties, a filter is designed in the framework of robust recursive estimation, and the developed filter algorithm is tested on the IEEE benchmark power system to demonstrate its effectiveness.

Assimilating temperature and salinity profile observations using an anisotropic recursive filter in a coastal ocean model

In order to improve the ocean forecasting in the North Sea and Baltic Sea, an assimilation scheme based on a bottom-topography-dependent anisotropic recursive filter has been used in this study. This scheme can stretch or flatten the shape of a local representative contour surface of the background error covariance function into the form of an ellipse. Furthermore, the computing efficiency has been largely improved due to implicit computation of the background error covariance. A two-month experiment has been used for verifying the impact of assimilating ocean profile observations on ocean forecasting. The results indicate that the use of temperature and salinity profiles can largely improve the oceanic forecasting. The root mean square differences between the forecasts and observations for temperature and salinity have been reduced by 36% and 18% in the experiment period, respectively. Moreover, it is found that the anisotropic recursive filter approach is especially efficient in areas with complex coastlines and sharp fronts, e.g., inner Danish waters. The results also show that the propagation of observation information from an observation position to its neighboring grid points is closely related to currents.

An Active Recursive RF Filter in 0.35 μm BiCMOS

An active recursive filter approach is proposed for the implementaion of an inductorless, tuneable RF filter in BiCMOS. A test circuit was designed and manufactured in a 0.35 ?m SiGe BiCMOS technology. In simulations, the feasibility of this type of filter was demonstrated and reasonably good performance was obtained. The simulations show a center frequency tuning range from 6 to 9.4 GHz and a noise figure of 8.8 to 10.4 dB depending on center frequency. Gain and Q-value are tunable in a wide range. Simulated IIP-3 and 1-dB compression point is ?26 and ?34 dBm respectively, simulated at the center frequency 8.5 GHz and with 15 dB gain. Measurements on the fabricated device shows a center frequency tuning range from 6.6 to 10 GHz, i.e. slightly higher center frequencies were measured than the simulated.

Real-Time Adaptive Contrast Enhancement

A recursive filter approach is introduced to simplify real-time implementation of an adaptive contrast enhancement scheme for imaging sensors. With this scheme, even scenes possessing large global dynamic ranges (>40 dB) can be accommodated by the limited dynamic range (20 dB) of a display without losing the local contrast essential for image interpretation. This paper describes the recursive filter implementation of the local area contrast enhancement scheme using charge-coupled devices and the resultant real-time hardware capable of processing standard 525 and 875 line TV compatible video (from vidicons, videotape recorders, etc). Several examples from video imagery are included to demonstrate its effectiveness.

2-D recursive phase filters for the solution of two-dimensional wave equations

This paper deals with two-dimensional wave equation migration in seismic Signal processing according to a frequency domain model. The required ideal phase specifications are approximated by a 2-D all-pass digital filter with symmetric half-plane hyperbolic impulse response. Examples are included to compare this recursive filtering approach with existing processing techniques.

Alternate methods of harmonic analysis

A modification of the traditional least square estimate is introduced and applied to the harmonic analysis of noisy periodic waveforms. The recursive Kalman filter approach is reviewed and the two methods are compared. The modified least square estimate is found to be a more efficient computational technique when the form of the signal allows its use.