23 results on '"SAMPLING errors"'
Search Results
2. Hierarchical tracking task control in redundant manipulators with compliance control in the null-space.
- Author
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Karami, Abbas, Sadeghian, Hamid, Keshmiri, Mehid, and Oriolo, Giuseppe
- Subjects
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AUTOMATIC control of redundant manipulators , *TRACKING control systems , *COMPUTER algorithms , *SAMPLING errors , *HUMAN-robot interaction - Abstract
Abstract In this paper, a new approach for dealing with multiple tracking tasks during physical interaction is proposed. By using this method, multiple tasks are accomplished based on the assigned priority in addition to a compliant behavior in the null-space of the main tasks. This issue is critical when robots are employed for complex manipulation in unknown environments and in the presence of human. During the manipulation in the dynamic environments, different objects may collide with the robot body and disturb its manipulation. In these cases, the robot is expected to continue execution of the tasks, accurately. Meanwhile, the robot should be compliant to ensure the safety during the interaction. A nonlinear controller-observer is proposed for tracking the desired trajectory based on a preallocated hierarchy. The suggested controller-observer estimates the external torques applied to the robot body without using joint torque measurements and compensates its projection on the task spaces. Asymptotic stability of the task space errors, the null-space velocity and the external interaction estimation error during accomplishing multiple tracking tasks are shown analytically. Finally, the algorithm performance is shown through experiments on a 7-DOF KUKA LWR robot arm. [ABSTRACT FROM AUTHOR]
- Published
- 2018
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3. Bounded-error optimal experimental design via global solution of constrained min–max program.
- Author
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Walz, Olga, Djelassi, Hatim, Caspari, Adrian, and Mitsos, Alexander
- Subjects
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CONSTRAINED optimization , *EXPERIMENTAL design , *SAMPLING errors , *COMPUTER algorithms , *MEMBERSHIP functions (Fuzzy logic) - Abstract
We present an improvement of existing methods for globally solving optimal experimental design (OED) for bounded-error estimation based on a bilevel formulation from Mukkala et al. (2017). The proposed solution method for the min–max program is based on our method for generalized semi-infinite programs (via restriction of the right-hand side). The algorithm employed has the advantage that it guarantees a global solution for the OED assuming the global solution of two subproblems. To obtain a feasible solution only the lower-level problem has to be solved globally. In case of a local solution of the upper-level problem, the solution is still feasible though it is an upper bound of the global solution. The min–max method for OED is illustrated with four examples: two simple chemical reactions, BET-adsorption and a reformulated predator-prey system. The benefits of global methods are shown along with the limitations of state-of-the-art global solvers. [ABSTRACT FROM AUTHOR]
- Published
- 2018
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4. Consensus of linear multi-agent systems via reduced-order observer.
- Author
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Yang, Tingting, Zhang, Pengfei, and Yu, Shuanghe
- Subjects
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MULTIAGENT systems , *SPANNING trees , *COMPUTER algorithms , *MATRICES (Mathematics) , *SAMPLING errors - Abstract
This paper is concerned with the consensus problem for continuous time linear multi-agent systems with directed communication topology. An estimation error dynamic is proposed to design the reduced-order observer for dealing with unknown coefficient matrices. To guarantee the consensus, control protocols based on the reduced-order observer are designed to utilize the relative outputs of neighboring agents whose communication topology contains a directed spanning tree. Then the algorithms are extended to the model reference consensus case, where the case of zero control input for reference node is not assumed. Numerical examples are given to illustrate the effectiveness of proposed approaches. [ABSTRACT FROM AUTHOR]
- Published
- 2017
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5. Optimal recursive estimation for networked descriptor systems with packet dropouts, multiplicative noises and correlated noises.
- Author
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Wang, Xin and Sun, Shuli
- Subjects
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DESCRIPTOR systems , *SAMPLING errors , *COVARIANCE matrices , *TECHNOLOGICAL innovations , *COMPUTER algorithms - Abstract
This paper addresses the optimal linear estimation problem for a class of networked descriptor systems with multiple packet dropouts, measurement multiplicative noises and finite-step correlated process and measurement noises. Based on a fast–slow subsystem decomposition approach (FSD), the descriptor system is transformed into two reduced-order linear nonsingular subsystems with finite-step correlated noises. Optimal linear estimators including filter, predictor and smoother with corresponding estimation error covariance matrices for the states and noises of new systems are developed via the innovation analysis approach. Then, the optimal linear estimators are obtained for the original descriptor system. An example shows the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]
- Published
- 2017
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6. Distributed Coverage Estimation and Control for Multirobot Persistent Tasks.
- Author
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Palacios-Gasos, Jose Manuel, Montijano, Eduardo, Sagues, Carlos, and Llorente, Sergio
- Subjects
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MOBILE robot control systems , *SAMPLING errors , *ROBOTIC path planning , *ROBOTIC trajectory control , *COMPUTER algorithms , *RANDOM fields - Abstract
In this paper, we address the problem of persistently covering an environment with a group of mobile robots. In contrast to traditional coverage, in our scenario the coverage level of the environment is always changing. For this reason, the robots have to continually move to maintain a desired coverage level. In this context, our contribution is a complete approach to the problem, including distributed estimation of the coverage and control of the motion of the robots. First, we present an algorithm that allows every robot to estimate the global coverage function only with local information. We pay special attention to the characterization of the algorithm, establishing bounds on the estimation error, and we demonstrate that the algorithm guarantees a perfect estimation in particular areas. Second, we introduce a new function to determine the possible improvement of the coverage at each point of the environment. Upon this metric, we build a motion control strategy that drives the robots to the points of the highest improvement while following the direction of the gradient of the function. Finally, we simulate the proposal to test its correctness and performance. [ABSTRACT FROM AUTHOR]
- Published
- 2016
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7. Adaptive estimation algorithm of boost-phase trajectory using binary asynchronous observation.
- Author
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Wu, Nan, Chen, Lei, Lei, Yongjun, and Meng, Fankun
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ADAPTIVE filters ,COMPUTER algorithms ,SAMPLING errors ,INFORMATION processing ,QUANTITATIVE research - Abstract
A kind of adaptive filter algorithm based on the estimation of the unknown input is proposed for studying the adaptive adjustment of process noise variance of boost phase trajectory. Polynomial model is used as the motion model of the boost trajectory, truncation error is regarded as an equivalent to the process noise and the unknown input and process noise variance matrix is constructed from the estimation value of unknown input according to the quantitative relationship among the unknown input, the state estimation error, and optimal process noise variance. The simulation results show that in the absence of prior information, the unknown input is estimated effectively in terms of magnitude, a positive definite matrix of process noise covariance which is close to the optimal value is constructed real-timely, and the state estimation error approximates the error lower bound of the optimal estimation. The estimation accuracy of the proposed algorithm is similar to that of the current statistical model algorithm using accurate prior information. [ABSTRACT FROM AUTHOR]
- Published
- 2016
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8. Optimal bilinear observers for bilinear state-space models by interaction matrices.
- Author
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Phan, Minh Q., Vicario, Francesco, Longman, Richard W., and Betti, Raimondo
- Subjects
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OPTIMAL control theory , *MATRICES (Mathematics) , *KALMAN filtering , *SAMPLING errors , *COMPUTER algorithms - Abstract
This paper formulates optimal bilinear observers for bilinear state-space models. Observers in bilinear form, as opposed to other nonlinear forms, are required to develop an extension of observer/Kalman filter identification for simultaneous identification of a bilinear state-space model and an associated bilinear observer from noisy input–output measurements. The paper establishes the relationship between the bilinear observer gains and the interaction matrices which are used to convert the original bilinear state-space model to a form that simplifies the identification of such a model. Techniques to find the interaction matrices are developed. In the absence of noises, these matrices produce the gains of the fastest converging observer. In the presence of noises, they minimise the state estimation error in the same manner as a standard steady-state Kalman filter. Numerical examples illustrate both the theoretical and computational aspects of the proposed algorithms. [ABSTRACT FROM PUBLISHER]
- Published
- 2015
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9. Covariance-Based DoA Estimation in a Krylov Subspace.
- Author
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Ferreira, Tadeu, Campos, Marcello, and Netto, Sergio
- Subjects
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DIRECTION of arrival estimation , *KRYLOV subspace , *SAMPLING errors , *COMPUTER simulation , *COMPUTATIONAL complexity , *COMPUTER algorithms - Abstract
Covariance-based DoA estimation (CB-DoA) algorithms represent lower computational complexity alternatives to the traditional ESPRIT approach. This paper investigates CB-DoA using Krylov-subspace techniques (including Arnoldi's and Lanczos' updates) with respect to the resulting computational cost and estimation error performance. The proposed modifications also allow an automatic estimation of the number of sources. Computational analyses performed for the resulting CB-DoA algorithm indicate cost savings above $$60\,\%$$ in comparison with the standard CB-DoA implementation, which already represents a $$20\,\%$$ improvement upon its ESPRIT counterpart, at an equivalent mean squared error level, as verified in numerical simulations. [ABSTRACT FROM AUTHOR]
- Published
- 2015
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10. Improved selectivity estimator for XML queries based on structural synopsis.
- Author
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Mohammed, Salahadin, El-Alfy, El-Sayed, and Barradah, Ahmad
- Subjects
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XML (Extensible Markup Language) , *QUERY languages (Computer science) , *SAMPLING errors , *COMPUTER algorithms , *PATTERN matching , *DATABASES - Abstract
With the increasing popularity of XML database applications, the use of efficient XML query optimizers is becoming very essential. The performance of an XML query optimizer depends heavily on the query selectivity estimators it uses to find the best possible query execution plan. In this work, we propose and evaluate a novel selectivity estimator, based on a structural synopsis, called SynopTech. The main idea of SynopTech is the generation of a summary tree by labeling the nodes of the source XML data tree using a fingerprint function and merging subtrees with similar structures. The generated summary tree is then used by SynopTech to estimate the selectivity of given queries. We experimented the proposed approach with four benchmark datasets of different structural characteristics and using different types of queries. Comparing with the Sampling algorithm, one of the state-of-the-art algorithms for selectivity estimations, SynopTech achieved lower selectivity estimation error rates, yet with very low memory budget. For example, for linear and existential queries, SynopTech had perfect estimations whereas the Sampling algorithm had an error rate of up to 70 %. For regular twig queries, SynopTech had a maximum error rate of 4.12 % whereas the Sampling algorithm had more than 55 %. [ABSTRACT FROM AUTHOR]
- Published
- 2015
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11. Enhanced filtered-x least mean M-estimate algorithm for active impulsive noise control.
- Author
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Sun, Guohua, Li, Mingfeng, and Lim, Teik C.
- Subjects
- *
ACTIVE noise control , *COMPUTER algorithms , *GAUSSIAN distribution , *ROBUST control , *SAMPLING errors , *SIGNAL processing - Abstract
Applying the typical active noise control (ANC) algorithm, namely the filtered-x least mean square (FxLMS) algorithm, to the non-Gaussian impulsive noise will experience difficulty. This is because the FxLMS algorithm is based upon the minimization of variance of the error signal, which is normally with the assumption of Gaussian distribution. To deal with this issue, the filtered-x least mean M -estimate (FxLMM) algorithm has been proposed based on the robust statistics theory to minimize the objective M -estimator error function. The Hampel’s three-part M -estimator has been applied to provide thresholds for detecting and rejecting the outliers/impulses in the error signal. To further enhance the robustness of the conventional FxLMM algorithm, in this study a modified FxLMM (MFxLMM) algorithm is proposed, where the reference signal is fed through another Hampel’s three-part M -estimator by setting thresholds for the impulsive samples. Numerous computer simulations are performed to validate the performance of the proposed algorithm. The generic impulsive noise is synthesized by using the contaminated Gaussian (CG) noise model, where certain number of impulses with different amplitudes and widths can be added in the background white Gaussian noise to represent different impulsive characteristics. In addition, extensive laboratory tests are carried out to further demonstrate the enhanced efficacy of the algorithm in a real-time environment. Results indicate that the proposed MFxLMM algorithm with online-threshold estimation is a very promising alternative for ANC of impulsive noise, and it shows similar convergence performance as the prevalent FxLMS algorithm for noise not corrupted by impulses. [ABSTRACT FROM AUTHOR]
- Published
- 2015
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12. Sufficient conditions for generalized Orthogonal Matching Pursuit in noisy case.
- Author
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Li, Bo, Shen, Yi, Wu, Zhenghua, and Li, Jia
- Subjects
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GENERALIZATION , *ORTHOGONAL polynomials , *COMPRESSED sensing , *ORTHOGONAL matching pursuit , *COMPUTER algorithms , *SAMPLING errors - Abstract
In compressive sensing, generalized Orthogonal Matching Pursuit (gOMP) algorithm generalizes OMP algorithm by selecting N ( N ≥ 1 ) atoms in each iteration. In this paper, we propose restricted isometry constant based sufficient conditions for gOMP algorithm to correctly recover the support of significant components of signal when both measurement dictionary and measurement signal are contaminated with noise. Bound of estimation error is also derived. Upper bound of restricted isometry constant could be relaxed if the original sparse signal is strong decaying. When sparsity of original sparse signal is unavailable, we give a stopping criterion of gOMP algorithm to ensure correct support recovery. [ABSTRACT FROM AUTHOR]
- Published
- 2015
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13. The Analysis of the UKF-Based Navigation Algorithm during GPS Outage.
- Author
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Bistrovs, V. and Kluga, A.
- Subjects
KALMAN filtering ,GLOBAL Positioning System ,SAMPLING errors ,COMPUTER algorithms ,DATA fusion (Statistics) ,NONLINEAR functional analysis - Abstract
The unscented Kalman filter (UKF) became very attractive for the navigation sensors data fusion, because of algorithm significant accuracy and implementation advantages. The unscented Kalman filter is based on the unscented transform (UT) to perform the estimation of the system states. The main idea of the unscented transformation is following. It's more effective to approximate probability distribution function than arbitrary transformation or nonlinear function. The developed sensors data fusion algorithm using the UKF is considered in this work. This algorithm was applied for the state estimation of the loosely coupled GPS/INS integrated navigation system. GPS/INS integrated navigation system contains low cost inertial sensors and low cost GPS receiver. To demonstrate the estimation performance, the processing of sensors data was done using linear Kalman filter (KF), extended Kalman filter (EKF) and UKF. As a result, UKF has lower velocity estimation error than EKF during simulated GPS signal outage. [ABSTRACT FROM AUTHOR]
- Published
- 2013
- Full Text
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14. An investigation of the error budget of tropical rainfall accumulation derived from merged passive microwave and infrared satellite measurements.
- Author
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Chambon, Philippe, Jobard, Isabelle, Roca, Rémy, and Viltard, Nicolas
- Subjects
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RAINFALL anomalies , *METEOROLOGICAL precipitation , *SAMPLING errors , *RADIO wave propagation , *COMPUTER algorithms - Abstract
An ongoing change in the theoretical framework from deterministic to probabilistic satellite rainfall estimations emerges from applications that require an error associated with rain estimates. The error budget for accumulated rainfall consists of several terms; these terms are related to sampling, algorithmic and calibration errors. From a number of studies, various errors were derived which have improved our understanding of the different terms in this error budget. In this paper, a methodological effort leading to the evaluation of a Tropical Amount of Precipitation with an Estimation of ERrors (TAPEER) is presented. It involves first merging passive microwave instantaneous rain rates from the BRAIN algorithm together with infrared imagery to build rain accumulations, and then evaluating the different terms of the error budget using two techniques. A dedicated error model is used to evaluate sampling errors and a forward error propagation approach is used for the estimation of algorithmic and calibration errors. One of the main findings in this study is the large contribution of the sampling errors and the algorithmic errors of BRAIN on medium rain rates (2-10 mm h−1) in the total error budget. This methodology leads to the formulation of a satellite rainfall product called TAPEER-BRAIN. This product will be one of the operational rainfall products for the Megha-Tropiques mission and will provide 1°/1-day accumulated rainfall estimations and associated error over the whole tropical belt. Copyright © 2012 Royal Meteorological Society [ABSTRACT FROM AUTHOR]
- Published
- 2013
- Full Text
- View/download PDF
15. Analytical Framework for WLAN-Cellular Voice Handover Evaluation.
- Author
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Ali, Tariq MD and Saquib, Mohammad
- Subjects
WIRELESS LANs ,POCKET computers ,COMPUTER algorithms ,MONTE Carlo method ,TIME series analysis ,SAMPLING errors ,PERFORMANCE evaluation - Abstract
The development of handheld mobile terminals (MT) capable of operating over both Wireless Local Area Networks (WLAN) and the cellular media is an important step toward the evolution of next-generation integrated networks. For real-time applications like voice, efficient vertical handover (VH) algorithms are required in maintaining a seamless connectivity and an acceptable level of quality for mobile users in heterogeneous environments. This paper proposes an analytical method for evaluating the performance of a VH algorithm that relies on the Received Signal Strength Indicator (RSSI) samples. The system model is portrayed upon addressing relevant factors that affect the quality and continuity of a voice call, and a set of performance metrics is proposed. A rigorous signal strength time series is utilized in characterizing the instantaneous decision metrics, and a novel intermediary system model, namely the N-model, is proposed to capture the large-scale shadowing effects. The performance of a generic algorithm that relies on the RSSI, which itself is susceptible to estimation error, is evaluated for an MT roaming in and out of the WLAN coverage area. Results obtained using the analytical expressions are validated by comparing them to that obtained through Monte Carlo simulation. [ABSTRACT FROM PUBLISHER]
- Published
- 2013
- Full Text
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16. An Adaptive UKF Based SLAM Method for Unmanned Underwater Vehicle.
- Author
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Hongjian Wang, Guixia Fu, Juan Li, Zheping Yan, and Xinqian Bian
- Subjects
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ADAPTIVE Kalman filters , *REMOTE submersibles , *COMPUTER algorithms , *PROBLEM solving , *ESTIMATION theory , *FEATURE extraction , *SAMPLING errors - Abstract
This work proposes an improved unscented Kalman filter (UKF)-based simultaneous localization andmapping (SLAM) algorithm based on an adaptive unscented Kalman filter (AUKF) with a noise statistic estimator. The algorithm solves the issue that conventional UKF-SLAMalgorithms have declining accuracy, with divergence occurring when the prior noise statistic is unknown and time-varying. The new SLAM algorithm performs an online estimation of the statistical parameters of unknown system noise by introducing a modified Sage-Husa noise statistic estimator. The algorithm also judges whether the filter is divergent and restrains potential filtering divergence using a covariance matchingmethod. This approach reduces state estimation error, effectively improving navigation accuracy of the SLAM system. A line feature extraction is implemented through a Hough transform based on the ranging sonar model. Test results based on unmanned underwater vehicle (UUV) sea trial data indicate that the proposed AUKF-SLAM algorithm is valid and feasible and provides better accuracy than the standard UKF-SLAM system. [ABSTRACT FROM AUTHOR]
- Published
- 2013
- Full Text
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17. A Review of Error Estimation in Adaptive Quadrature.
- Author
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Gonnet, Pedro
- Subjects
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COMPUTER algorithms , *COMPUTER programming , *SAMPLING errors , *ERROR analysis in mathematics , *STATISTICAL sampling , *ESTIMATION theory - Abstract
The most critical component of any adaptive numerical quadrature routine is the estimation of the integration error. Since the publication of the first algorithms in the 1960s, many error estimation schemes have been presented, evaluated, and discussed. This article presents a review of existing error estimation techniques and discusses their differences and their common features. Some common shortcomings of these algorithms are discussed, and a new general error estimation technique is presented. [ABSTRACT FROM AUTHOR]
- Published
- 2012
- Full Text
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18. Optimal placement of mobile sensors for data assimilations.
- Author
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Kang, Wei and Xu, Liang
- Subjects
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COMPUTER algorithms , *PARAMETER estimation , *MONTE Carlo method , *GAUSSIAN distribution , *SAMPLING errors , *MATHEMATICAL optimization - Abstract
We explore the theoretical framework as well as the associated algorithms for the problem of optimally placing mobile observation platforms to maximise the improvement of estimation accuracy. The approach in this study is based on the concept of observability, which is a quantitative measure of the information provided by sensor data and user-knowledge. To find the optimal sensor locations, the observability is maximised using a gradient projection method. The Burgers equation is used to verify this approach. To prove the optimality of the sensor locations, Monte Carlo experimentations are carried out using standard 4D-Var algorithms based on two sets of data, one from equally spaced sensors and the other from the optimal sensor locations. The results show that, relative to equally spaced sensors, the 4D-Var data assimilation achieves significantly improved estimation accuracy if the sensors are placed at the optimal locations. A robustness study is also carried out in which the error covariance matrix is varied by 50% and the sensor noise covariance is varied by 100%. In addition, both Gaussian and uniform probability distributions are used for the sensor noise and initial estimation errors. In all cases, the optimal sensor locations result in significantly improved estimation accuracy. [ABSTRACT FROM AUTHOR]
- Published
- 2012
- Full Text
- View/download PDF
19. Frequency estimation of a sinusoidal signal via a three-point interpolated DFT method with high image component interference rejection capability.
- Author
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Belega, Daniel, Petri, Dario, and Dallet, Dominique
- Subjects
- *
SIGNAL frequency estimation , *DIGITAL signal processing , *DISCRETE Fourier transforms , *IMAGE analysis , *SAMPLING errors , *SIGNAL-to-noise ratio , *COMPUTER algorithms - Abstract
Abstract: This paper proposes a three-point Interpolated Discrete Fourier Transform (IpDFT) method for frequency estimation of a discrete-time sinusoidal signal. It is based on the maximum sidelobe decay (MSD) windows and is highly effective in rejecting the detrimental effect on the estimation accuracy due to the image component of the signal spectrum. This remarkable feature is achieved by using an analytical expression based on a suitable weighting of the three largest DFT spectrum samples. The proposed method provides good results when the effect of the spectral interference due to the image component dominates other estimation error sources. The accuracy of the proposed method and of other state-of-the-art methods such as the multi-point IpDFT methods and the four-parameter sine-fitting (4PSF) algorithm are compared through both computer simulations and experimental results in the case of ideal, noisy, and harmonically distorted sinusoids. A small number of acquired cycles is assumed in order to analyze situations in which the contribution from the image component interference is significant. The performed comparison shows that the proposed method outperforms the considered multi-point IpDFT methods when the Signal-to-Noise Ratio (SNR) is higher than 30 dB and the number of acquired cycles is enough small. The proposed method outperforms also the 4PSF algorithm when the frequency estimation error is dominated by harmonics rather than wideband noise. [Copyright &y& Elsevier]
- Published
- 2014
- Full Text
- View/download PDF
20. Low-complexity sinusoidal-assisted EMD (SAEMD) algorithms for solving mode-mixing problems in HHT.
- Author
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Shen, Wen-Chung, Chen, Yu-Hao, and Wu, An-Yeu (Andy)
- Subjects
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COMPUTATIONAL complexity , *COMPUTER algorithms , *HILBERT-Huang transform , *SAMPLING errors , *COEFFICIENTS (Statistics) , *INFORMATION storage & retrieval systems - Abstract
Abstract: In recent years, Hilbert–Huang Transform (HHT) is widely used to analyze nonlinear and non-stationary signals in various applications, such as seismic and biomedical signal processing. In HHT, the Empirical Mode Decomposition (EMD) is the key component for decomposing natural signals into intrinsic mode functions (IMFs). Since the EMD suffers from mode-mixing problem, in which some fast intermittent signals riding on a slow-oscillating wave, the Ensemble-EMD (EEMD) is proposed to solve this problem with the aids of noise. However, the EEMD requires high computational complexity in ensemble and is unsuitable for some real-time applications, such as ultrasound systems. In this paper, intermittent signals are modeled in mathematical forms for IMF decomposition. We then propose sinusoidal-assisted EMD (SAEMD) for efficient and effective HHT computation to solve mode-mixing problems. The type I of SAEMD (SAEMD-I) is initially proposed to solve the mode-mixing problem with very low computational complexity. However, if the maximum frequency of data is unknown in some real-world applications, the SAEMD-I may encounter estimation error caused by imprecise locations of extrema. For practical data, the type II of SAEMD (SAEMD-II) is proposed to solve the sampling rate issue. Compared with the ensemble-100 EEMD, the SAEMD-II can have 11–13 times improvement in terms of computation speed in El Niño application and comparable correlation coefficient (−0.95 at IMF 8). Hence, the proposed SAEMD-II scheme is a good candidate of implementing cost-effective HHT when computational complexity and real-time data processing are of major concern. [Copyright &y& Elsevier]
- Published
- 2014
- Full Text
- View/download PDF
21. Robust state estimation for jump Markov linear systems with missing measurements.
- Author
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Li, Wenling, Jia, Yingmin, Du, Junping, and Zhang, Jun
- Subjects
- *
ROBUST control , *ESTIMATION theory , *MARKOV processes , *LINEAR systems , *COMPUTER algorithms , *MATHEMATICAL models , *SAMPLING errors - Abstract
Abstract: This paper is concerned with the robust state estimation problem for a class of jump Markov linear systems (JMLSs) with missing measurements. Two independent Markov chains are used to describe the behavior of the system dynamics and the characteristic of missing measurements, respectively. A robust filtering algorithm is developed by applying the basic interacting multiple model (IMM) approach and the technique, which is different from the traditional Kalman filtering with minimum estimation error variance criterion. A maneuvering target tracking example is provided to demonstrate the effectiveness of the proposed algorithm. [Copyright &y& Elsevier]
- Published
- 2013
- Full Text
- View/download PDF
22. Non Adaptive Second-Order Generalized Integrator for Identification of a Biased Sinusoidal Signal.
- Author
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Fedele, Giuseppe and Ferrise, Andrea
- Subjects
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ADAPTIVE control systems , *SIGNAL frequency estimation , *SOUND measurement , *SAMPLING errors , *PARAMETER estimation , *LEAST squares , *COMPUTER algorithms - Abstract
This note presents a new algorithm that is designed to identify the frequency, magnitude, phase and offset of a biased sinusoidal signal. The structure of the algorithm includes an orthogonal system generator based on a second-order generalized integrator. The proposed strategy has the advantages of a fast and accurate signal reconstruction capability and a good rejection to noise. [ABSTRACT FROM PUBLISHER]
- Published
- 2012
- Full Text
- View/download PDF
23. On computing the shortest path in a multiply-connected domain having curved boundaries.
- Author
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Wei, Xiangzhi and Joneja, Ajay
- Subjects
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COMPUTER-aided design , *COMPUTER algorithms , *COMPUTATIONAL complexity , *COMMUNICATION , *ESTIMATION theory , *SAMPLING errors - Abstract
Abstract: This submission is a communication related to a recently published article in Computer-Aided Design journal, titled “Shortest path in a multiply-connected domain having curved boundaries”. We point out an error in estimating the time complexity of the algorithm proposed in that paper, using a simple example. We also illustrate, with a different example, that an ostensibly time-saving scheme used by that algorithm, called exterior region elimination, cannot be applied in general to derive the correct shortest interior path. Finally, we propose an alternate algorithm that solves the same problem with an improved worst-case running time. [Copyright &y& Elsevier]
- Published
- 2014
- Full Text
- View/download PDF
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