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CHIRP EXCITATION OF ULTRASONIC PROBES AND ALGORITHM FOR filtering TRANSIT TIMES IN HIGH-RANGEABILITY GAS-FLOW METERING

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IEEE TRANSACTIONS ON ULTRASONICS FERROELECTRICS AND FREQUENCY CONTROL 1993年第3期40卷 193-215页

作者： FOLKESTAD, T MYLVAGANAM, KS CHRISTIAN MICHELSEN INST DEPT SCI & TECHNOLULTRASON INSTRUMENTAT SECTN-5036 FANTOFTNORWAY

The signal processing used in an ultrasonic high-rangeability gas flow meter using times-of-light, is presented. The How meter under discussion, uses a combination of continuous wave and chirp signals to measure at low flow velocities, below 20 m/s, and chirp signals alone, to measure high flow velocities, above 20 m/s. Because of the necessity of pulse compression technique in the signal waveform design, a discussion on the technique of pulse compression and choice of signal waveforms is given. Hereby, the advantages and disadvantages of amplitude weighing vis-a-vis frequency domain manipulations of the waveforms are also discussed. The adaptive signal processing based on pulse compression is the most important feature in achieving high-rangeability ultrasonic gas How metering. The paper aims at an insight building and practical presentation of pulse compression. Via matched filtering, some fundamental properties of pulse compression are discussed. To eliminate spurious times-of-flight, the usage of special filtering technique is presented, based on assessing the gradient of ascendingly ordered time series of times-of-flight measurements. A summary of user experience of high-rangeability gas flow meters in use on different offshore platforms and in refineries, is given. Long term tests that looked into the accuracy of the high-rangeability flow meter are also described.

关键词： Chirp Probes filtering algorithms Signal processing algorithms Fluid flow Fluid flow measurement Velocity measurement Ultrasonic variables measurement Pulse compression methods Signal design

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Distributed Matrix-Weighted Fusion Consensus filtering With Two-Stage filtering for Sensor Networks

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IEEE SENSORS JOURNAL 2023年第5期23卷 5003-5013页

作者： Wang, Ruixi Sun, Shuli Heilongjiang Univ Sch Elect Engn Harbin Peoples R China

This article is concerned with a distributed consensus filtering problem in a sensor network (SN). In the SN, each sensor node plays a local fusion center. By diffusion and fusion of information among sensor nodes, estimates of all nodes in the SN tend to consensus. At each sensor node, a distributed consensus filtering algorithm with a two-stage filtering structure is devised. At each time, the first stage filtering is that each sensor node produces its local filter estimate based on its own measurement and previous fused estimate, and then transmits it to its neighbor nodes;and the second stage filtering is that each sensor node produces a fused estimate by using a matrix-weighted optimal fusion algorithm in the linear unbiased minimum variance (LUMV) criterion to iteratively fuse estimates of sensor itself and neighbor nodes, where cross-covariance matrices (CCMs) between sensor nodes at different fusion times are derived. Its estimation accuracy is gradually improved with the increase of fusion times and approximates a centralized fusion filter (CFF). To avoid calculation of CCMs between sensor nodes, a suboptimal distributed consensus filtering algorithm is also presented by minimizing an upper bound of the fusion filtering error covariance at each fusion in the second stage. It has reduced computational cost at the expense of accuracy loss. The performance of the proposed distributed consensus filters is analyzed. A tracking system is used to verify the effectiveness of the algorithms.

关键词： Sensors Sensor fusion Estimation filtering algorithms Information filters Kalman filters Covariance matrices Cross-covariance matrix (CCM) distributed consensus filtering linear unbiased minimum variance (LUMV) matrix-weighted fusion sensor network (SN)

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Real-time state estimator without noise covariance matrices knowledge - fast minimum norm filtering algorithm

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IET CONTROL THEORY AND APPLICATIONS 2015年第9期9卷 1422-1432页

作者： Feng, Bo Ma, Hongbin Fu, Mengyin Yang, Chenguang Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China Beijing Inst Technol State Key Lab Intelligent Control & Decis Complex Beijing 100081 Peoples R China Univ Plymouth Robot & Neural Syst Plymouth PL4 8AA Devon England

The digital filtering technology has been widely applied in a majority of signal processing applications. For the linear systems with state-space model, Kalman filter provides optimal state estimates in the sense of minimum-mean-squared errors and maximum-likelihood estimation. However, only with accurate system parameters and noise statistical properties, the estimation obtained by standard Kalman filter is the optimal state estimate. Most of time, the exact noise statistical properties could not be obtained as a priori information or even wrong statistical properties may be captured by the offline method. This may lead to a poor performance (even divergence) of Kalman filtering algorithm. In this study, a novel real-time filter, named as fast minimum norm filtering algorithm, has been proposed to deal with the case when the covariance matrices of the process and measurement noises were unknown in the linear time-invariant systems with state-space model. Tests have been performed on numerical examples to illustrate that the fast minimum norm filtering algorithm could be used to obtain acceptable precision state estimation in comparison with the standard Kalman filter for the discrete-time linear time-invariant systems.

关键词： Kalman filters State space models State-space methods filtering algorithms Noise measurement variance covariance matrix State estimation Time invariant systems Covariance matrix Linear system

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A Novel Adaptive filtering Algorithm Based Parameter Estimation Technique for Photovoltaic System

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IEEE TRANSACTIONS ON ENERGY CONVERSION 2022年第1期37卷 286-294页

作者： Qais, Mohammed H. Muyeen, S. M. Sanaa Univ Elect Engn Dept Sanaa 1247 Yemen Curtin Univ Sch Elect Engn Comp & Math Sci Bentley WA 6102 Australia

This paper offers a hybrid analytical and estimation based technique to determine the photovoltaic (PV) system parameter in a systematic way. The new model formulation is based on the datasheet parameters under standard test condition and normal operating cell temperature environment, forming the analytical approach's foundation. The estimation part is based on the adaptive filtering algorithm, which shows superiority in estimated parameters compared to existing techniques applied in the photovoltaic system. The proposed approach is made available for a single diode PV model and scaled up aggregated model in extracting the model parameters of two real PV modules in the market, representing the exact I-V characteristics of the manufacturer datasheet. A rigorous comparative analysis is carried out between two adaptive and two optimization based estimations for performance evaluation and recommendation purposes.

关键词： Adaptation models Mathematical model Photovoltaic systems filtering algorithms Estimation Iterative methods Analytical models Adaptive filtering algorithm partial shading photovoltaic array single diode model

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Robust Two-Stage filtering for INS/Gravity Matching Passive Integrated Navigation Systems

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IEEE SENSORS JOURNAL 2022年第4期22卷 3370-3381页

作者： Luo, Kaixin Cai, Shaokun Yu, Ruihang Cao, Juliang Guo, Yan Natl Univ Def Technol Coll Intelligence Sci & Technol Changsha 410073 Hunan Peoples R China

With the expansion of the exploration scope of underwater vehicles and the improvement of their safety requirements, integrated navigation under passive conditions has become a hot topic in the field of navigation. Due to the stability and detectability of the Earth's gravity field, the use of a matching result for external correction of inertial navigation systems (INSs) can be effective. For practical navigation applications, an inertial gravity matching integrated navigation algorithm must be designed for real-time applicability, diversified output information capability, and anti-interference capability. In this paper, an INS/gravity matching (INS-GM) integrated navigation algorithm is improved to extract the information of gravity fields in real time and correct the navigation results, and a robust integrated algorithm is proposed. Through two-hierarchy filtering, correction of the gravity field to the carrier position and of the integrated navigation system is obtained. According to the first-hierarchy filtering noise characteristics of gravity matching, the robustness of the algorithm to a complex environment is improved by second-hierarchy filtering. The ocean experiment data processing results show that the algorithm can effectively handle a certain degree of initial error, and an integrated navigation accuracy of 205 m is achieved in a good experimental environment. In the case of severe test conditions, the accuracy of the robust integrated navigation algorithm is 72% higher than that of the ordinary navigation algorithm, and it has a strong anti-jamming ability, which can meet the requirements of marine inertial gravity matching integrated navigation tasks.

关键词： Gravity Navigation Satellite navigation systems Real-time systems filtering algorithms Mathematical models Sensors Gravimetry gravity matching robust filtering point mass filtering (PMF) integrated navigation

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A ROBUST filtering ALGORITHM FOR SUBPIXEL RECONSTRUCTION OF CHAIN CODED LINE DRAWINGS

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 1987年第3期9卷 451-457页

作者： KOPLOWITZ, J RAJ, APS Department of Electrical and Computer Engineering Clarkson University Potsdam NY 13676

A robust algorithm is presented for smoothing and achieving subpixel accuracy in the reconstruction of chain coded line drawings. The algorithm does not remove sharp corners and does not need a priori knowledge of curvature statistics. A fast on-line implementation can be achieved using a table look-up. A simplified algorithm can be used for reconstructing digitized polygons.

关键词： Robustness filtering algorithms Image reconstruction Image edge detection Quantization Engineering drawings Algorithm design and analysis Numerical analysis Fourier transforms Optical filters Author Keywords subpixel accuracy Chain coding estimation of digital lines filtering line drawings

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Adaptive filtering strategy for numerical constraint satisfaction problems

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EXPERT SYSTEMS WITH APPLICATIONS 2015年第21期42卷 8086-8094页

作者： Araya, Ignacio Soto, Ricardo Crawford, Broderick Pontificia Univ Catolica Valparaiso Valparaiso Chile Univ Autonoma Chile Santiago Chile Univ Cientif Sur Lima Peru Univ San Sebastian Concepcion Chile Univ Cent Chile Santiago Chile

The reliability and increasing performance of search-tree-based interval solvers for solving numerical systems of constraints make them applicable to various expert system domains. filtering methods are applied in each node of the search tree to reduce the variable domains without the loss of solutions. Current interval-based solvers generally leave it up to the solver designer to decide which set of filtering methods to apply to solve a particular problem. In this work, we propose an adaptive strategy to dynamically determine the set of filtering methods that will be applied in each node of the search tree. Our goal is twofold: first, we want to simplify the task of the solver designer, and second, we believe that an adaptive strategy may improve the average performance of the current state-of-the-art strategies. The proposed adaptive mechanism attempts to avoid calling costly filtering methods when their probability of filtering domains is low. We assume that fruitful filtering occurs in nearby revisions or clusters. Thus, the decision about whether or not to apply a filtering method is based on a cluster detection mechanism. When a cluster is detected, the associated methods are consecutively applied in order to exploit the cluster. Alternately, in zones without clusters, only a cheap method is applied, thus reducing the filtering effort in large portions of the search. We compare our approach with state-of-the-art strategies, demonstrating its effectiveness. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Interval-based solvers Branch and bound filtering algorithms Consistency techniques

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FINITE-PRECISION ANALYSIS OF A COVARIANCE ALGORITHM FOR LEAST-SQUARES FIR filtering AND AR MODELING

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 1993年第10期41卷 2990-3002页

作者： GLENTIS, GO KALOUPTSIDIS, N Department of Informatics Division of Communications and Signal Processing University of Athens (NKUA) Athens Greece

In this paper a numerically stable, fast-order recursive algorithm for the solution of the covariance approach in signal modeling is described. The propagation of finite arithmetic errors as well as data acquisition errors are studied in detail. First, linearization of the main algorithmic recursions is carried out. Then, a suitable transformation converts the resulting state equations of the accumulated errors into their residual form. Subsequently, bounds for the residuals are computed. The derivation of these bounds heavily depends on the Levinson type structure of the algorithm and the low displacement rank of the problem. The main result of the paper then states that the proposed algorithm is numerically weakly stable. The proposed order recursive algorithm is subsequently utilized as a block adaptive method. Its performance is also demonstrated by long run simulations.

关键词： Finite impulse response filter Algorithm design and analysis filtering algorithms Least squares methods Signal processing algorithms Equations Linear systems Data acquisition Autocorrelation Arithmetic

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AN EFFICIENT RECURSIVE TOTAL LEAST-SQUARES ALGORITHM FOR FIR ADAPTIVE filtering

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 1994年第2期42卷 268-280页

作者： DAVILA, CE Electrical Engineering Department Southem Methodist University Dallas TX USA

An algorithm for recursively computing the total least squares (TLS) solution to the adaptive filtering problem is described. This algorithm requires O(N) multiplications per iteration to effectively track the N-dimensional eigenvector associated with the minimum eigenvalue of an augmented sample covariance matrix. It is shown that the recursive least squares (RLS) algorithm generates biased adaptive filter coefficients when the filter input vector contains additive noise. The TLS solution on the other hand, is seen to produce unbiased solutions. Examples of standard adaptive filtering applications that result in noise being added to the adaptive filter input vector are cited. Computer simulations comparing the relative performance of RLS and recursive TLS are described.

关键词： Least squares methods Finite impulse response filter Adaptive filters Resonance light scattering filtering algorithms Eigenvalues and eigenfunctions Covariance matrix Additive noise Application software Computer simulation

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NEURAL-NETWORK-BASED ADAPTIVE MATCHED filtering FOR QRS DETECTION

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IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 1992年第4期39卷 317-329页

作者： XUE, QZ HU, YH TOMPKINS, WJ UNIV WISCONSIN DEPT ELECT & COMP ENGN MADISON WI 53706 USA

We have developed an adaptive matched filtering algorithm based upon an artificial neural network (ANN) for QRS detection. We use an ANN adaptive whitening filter to model the lower frequencies of the ECG which are inherently nonlinear and nonstationary. The residual signal which contains mostly higher frequency QRS complex energy is then passed through a linear matched filter to detect the location of the QRS complex. We developed an algorithm to adaptively update the matched filter template from the detected QRS complex in the ECG signal itself so that the template can be customized to an individual subject. This ANN whitening filter is very effective at removing the time-varying, nonlinear noise characteristic of ECG signals. Using this novel approach, the detection rate for a very noisy patient record in the MIT/BIH arrhythmia database is 99.5%, which compares favorably to the 97.5% obtained using a linear adaptive whitening filter and the 96.5% achieved with a bandpass filtering method.

关键词： Adaptive filters Matched filters Artificial neural networks Electrocardiography Frequency Band pass filters Nonlinear filters filtering algorithms Adaptive signal detection Databases

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