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FDA-MIMO for joint angle and range estimation: unfolded coprime framework and parameter estimation algorithm

IET RADAR SONAR AND NAVIGATION 2020年第6期14卷 917-926页

作者： Wang, Cheng Li, Zheng Zhang, Xiaofei Nanjing Univ Aeronaut & Astronaut Coll Elect & Informat Engn Jiangjun Rd 29 Nanjing Peoples R China

The frequency diverse array multiple-input multiple-output (FDA-MIMO) radar provides range estimation capability by exploiting a small frequency offset across the transmit sensors, which has been utilised in numerous applications. However, the estimation performance is basically limited by the array geometry and signal bandwidth. In this study, the authors propose a new FDA-MIMO framework, i.e. the unfolded coprime array with 'unfolded' coprime frequency offsets (UCA-UCFO) framework, for joint angle and range estimation without ambiguity. The array aperture and signal bandwidth are obviously expanded by employing UCA in the spatial domain and frequency domain, which results in significantly enhanced estimation accuracy and resolution. In addition, we construct the joint angle and range estimation problem as a two-dimensional (2D)-multiple signal classification spatial spectrum and transform 2D total spectrum search into a 1D local spectrum search by introducing a successive iteration (SUIT) algorithm. The SUIT algorithm can significantly relieve the computational burden but without performance degradation. The Cramer-Rao bounds of angle and range are provided as a performance benchmark. The analysis and simulations have validated the superiority and advantages of the UCA-UCFO framework and SUIT algorithm with respect to location accuracy, resolution, and computational complexity.

关键词： parameter estimation radar signal processing MIMO radar MIMO communication direction-of-arrival estimation array signal processing antenna arrays computational complexity signal classification joint angle unfolded coprime framework parameter estimation algorithm frequency diverse array multiple-input multiple-output range estimation capability estimation performance array geometry signal bandwidth FDA-MIMO framework unfolded coprime array unfolded coprime frequency offsets array aperture enhanced estimation accuracy range estimation problem 2D total spectrum search successive iteration algorithm SUIT algorithm UCA-UCFO framework

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parameter estimation algorithms for dynamical response signals based on the multi-innovation theory and the hierarchical principle

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IET SIGNAL PROCESSING 2017年第2期11卷 228-237页

作者： Xu, Ling Ding, Feng Jiangnan Univ Sch Internet Things Engn Minist Educ Key Lab Adv Proc Control Light Ind Wuxi 214122 Peoples R China Wuxi Vocat Inst Commerce Sch Internet Things Technol Wuxi 214153 Peoples R China

In this study, the authors consider the parameter estimation problem of the response signal from a highly non-linear dynamical system. The step response experiment is taken for generating the measured data. Considering the stochastic disturbance in the industrial process and using the gradient search, a multi-innovation stochastic gradient algorithm is proposed through expanding the scalar innovation into an innovation vector in order to obtain more accurate parameter estimates. Furthermore, a hierarchical identification algorithm is derived by means of the decomposition technique and interaction estimation theory. Regarding to the coupled parameter problem between subsystems, the authors put forward the scheme of replacing the unknown parameters with their previous parameter estimates to realise the parameter estimation algorithm. Finally, several examples are provided to access and compare the behaviour of the proposed identification techniques.

关键词： parameter estimation gradient methods estimation theory signal processing stochastic processes parameter estimation algorithm dynamical response signal multiinnovation theory hierarchical principle nonlinear dynamical system stochastic disturbance industrial process gradient search multiinnovation stochastic gradient algorithm scalar innovation innovation vector hierarchical identification algorithm decomposition technique interaction estimation theory

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Recursive coupled projection algorithms for multivariable output-error-like systems with coloured noises

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IET SIGNAL PROCESSING 2020年第7期14卷 455-466页

作者： Pan, Jian Ma, Hao Zhang, Xiao Liu, Qinyao Ding, Feng Chang, Yufang Sheng, Jie Hubei Univ Technol Sch Elect & Elect Engn Hubei Key Lab High Efficiency Utilizat Solar Ener Wuhan 430068 Peoples R China Jiangnan Univ Sch Internet Things Engn Wuxi 214122 Jiangsu Peoples R China Univ Washington Sch Engn & Technol Tacoma WA 98402 USA

By combining the coupling identification concept with the gradient search, this study develops a partially coupled generalised extended projection algorithm and a partially coupled generalised extended stochastic gradient algorithm to estimate the parameters of a multivariable output-error-like system with autoregressive moving average noise from input-output data. The key is to divide the identification model into several submodels based on the hierarchical identification principle and to establish the parameter estimation algorithm by using the coupled relationship between these submodels. The simulation test results indicate that the proposed algorithms are effective.

关键词： identification parameter estimation least squares approximations gradient methods autoregressive moving average processes state-space methods stochastic processes multivariable control systems recursive coupled projection algorithms multivariable output-error-like system coloured noises coupling identification concept gradient search projection algorithm stochastic gradient algorithm autoregressive moving average noise input-output data identification model hierarchical identification principle parameter estimation algorithm coupled relationship

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estimation of Auto-Regressive models for time series using Binary or Quantized Data

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IFAC-PapersOnLine 2018年第15期51卷 581-586页

作者： Auber, R. Pouliquen, M. Pigeon, E. M'Saad, M. Gehan, O. Chapon, P.A. Moussay, S. Laboratoire d'Automatique de Caen - EA 7478 Normandie Univ UNICAEN ENSICAEN LAC Caen Cedex14050 France Bodycap 3 rue du Dr Laennec Caen14000 France

In this paper, we first present an algorithm for the estimation of an Auto-Regressive model of time series using output data of a binary sensor. This algorithm is based on the estimation of the autocorrelation of time series for a threshold different from zero. The algorithm is then extended to time series with several quantization levels. Simulation results are given to show the effectiveness of the proposed approaches. © 2018

关键词： Time series analysis Pressure measurement Structural analysis Time series Auto regressive models Binary sensors Output data parameter estimation algorithm Quantization levels Quantized Data

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estimation of Auto-Regressive models for time series using Binary or Quantized Data

Estimation of Auto-Regressive models for time series using B...

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18th IFAC Symposium on System Identification (SYSID)

作者： Auber, R. Pouliquen, M. Pigeon, E. M'Saad, M. Gehan, O. Chapon, P. A. Moussay, S. Normandie Univ Lab Automat Caen EA 7478 UNICAENENSICAEN F-14050 Caen France Bodycap 3 Rue Dr Laennec F-14000 Caen France

关键词： Auto-Regressive model Time series Binary Sensor Quantized Data parameter estimation algorithm

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Efficient algorithms for real-time monitoring of transmission line parameters and their performance with practical synchrophasors

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IET GENERATION TRANSMISSION & DISTRIBUTION 2017年第5期11卷 1134-1143页

作者： Gurusinghe, Dinesh Rangana Rajapakse, Athula D. Univ Manitoba Dept Elect & Comp Engn Winnipeg MB R3T 5V6 Canada

Accurate transmission line parameters are important for many applications that ensure reliable operation of a power system. The traditional theoretical calculations and offline measurements are widely used approaches obtaining line parameters, but they do not allow tracking of the parameters that change with the environmental factors and load conditions. Synchrophasor-based real-time transmission line parameter monitoring algorithms can track the changing parameters. In this study, two novel line parameter estimation algorithms: a lump parameter model and a distributed parameter model are proposed. The performance of the new algorithms are evaluated under various operating conditions using a real-time digital simulator, and compared with six existing algorithms in terms of both accuracy and computational efficiency. The algorithms were also tested and compared using synchrophasor data obtained from a hardware experimental setup. Furthermore, application of the algorithms to an actual 230kV transmission line is demonstrated. Finally, the sensitivity of the estimated parameters to bias errors in the measurements is analysed.

关键词： power transmission lines phasor measurement parameter estimation environmental factors load conditions synchrophasor-based real-time transmission line parameter monitoring algorithm parameter estimation algorithm distributed parameter model real-time digital simulator voltage 230 kV

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Vehicle tyre/road interaction modeling and identification of its parameters using real-time trust-region methods

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IFAC-PapersOnLine 2016年第3期49卷 111-116页

作者： Sharifzadeh, Mojtaba Akbari, Ahmad Timpone, Francesco Daryani, Reza Institute of Automatic Control Sahand University of Technology Tabriz Iran Dipartimento di Ingegneria Industriale Universit‘a degli Studi di Napoli Federico II Napoli Italy Faculty of Mechanical Engineering Istanbul Technical University Istanbul Turkey

Driving safety can be achieved by better understanding critical situations which may require the knowledge of interaction between vehicle tyres and the road surfaces. It is thus essential to have a good estimation of the tyre/road friction parameters in real-time. The paper deals with the trust-region based method for on-line estimation of tyre/road friction parameters. This method provides an appropriate modeling (of a vehicle and the tyre/road contact) to observe the tyre/road friction coefficients directly using measurable signals in real-time. In this work, we present a new LuGre model-based nonlinear least squares (NLLS) parameter estimation algorithm using vehicle dynamic to obtain the parameters of LuGre model based on recursive nonlinear optimization of the curve fitting errors. The proposed estimation method can also be utilized in large-scale problems with similar conditions. Very promising results have been obtained in real-time simulations for most of the driving and road situations. © 2016

关键词： parameter estimation Curve fitting Friction Nonlinear analysis Nonlinear programming Roads and streets Tires Tribology Friction coefficients Friction estimation Non linear least squares Non linear optimization parameter estimation algorithm Real time simulations Recursive identification Vehicle dynamics

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Toroidal Information Fusion Based on the Bivariate von Mises Distribution

Toroidal Information Fusion Based on the Bivariate von Mises...

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IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)

作者： Kurz, Gerhard Hanebeck, Uwe D. Karlsruhe Inst Technol Intelligent Sensor Actuator Syst Lab ISAS Inst Anthropomat & Robot D-76021 Karlsruhe Germany

ISBN: (纸本)9781479977727

Fusion of toroidal information, such as correlated angles, is a problem that arises in many fields ranging from robotics and signal processing to meteorology and bioinformatics. For this purpose, we propose a novel fusion method based on the bivariate von Mises distribution. Unlike most literature on the bivariate von Mises distribution, we consider the full version with matrix-valued parameter rather than a simplified version. By doing so. we are able to derive the exact analytical computation of the fusion operation. We also propose an efficient approximation of the normalization constant including an error bound and present a parameter estimation algorithm based on a maximum likelihood approach. The presented algorithms are illustrated through examples.

关键词： matrix algebra maximum likelihood estimation sensor fusion statistical distributions bivariate von Mises distribution correlated angle fusion operation matrix-valued parameter maximum likelihood approach normalization constant parameter estimation algorithm toroidal information fusion Approximation algorithms Approximation methods Gaussian distribution Maximum likelihood estimation Optimization parameter estimation Robots maximum likelihood estimation parameter estimation Bivariate Approximation method statistical distribution matrix algebra Sensor fusion Approximation algorithms Gaussian distribution Robot Information fusion ANNULAR

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New approach to phase correction in multi-echo T₂ relaxometry

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JOURNAL OF MAGNETIC RESONANCE 2014年 249卷 100-107页

作者： Bjork, Marcus Stoica, Petre Uppsala Univ Dept Informat Technol SE-75105 Uppsala Sweden

estimation of the transverse relaxation time, T-2, from multi-echo spin-echo images is usually performed using the magnitude of the noisy data, and a least squares (LS) approach. The noise in these magnitude images is Rice distributed, which can lead to a considerable bias in the LS-based T-2 estimates. One way to avoid this bias problem is to estimate a real-valued and Gaussian distributed dataset from the complex data, rather than using the magnitude. In this paper, we propose two algorithms for phase correction which can be used to generate real-valued data suitable for LS-based parameter estimation approaches. The first is a Weighted Linear Phase estimation algorithm, abbreviated as WELPE. This method provides an improvement over a previously published algorithm, while simplifying the estimation procedure and extending it to support multi-coil input. The algorithm fits a linearly parameterized function to the multi-echo phase-data in each voxel and, based on this estimated phase, projects the data onto the real axis. The second method is a maximum likelihood estimator of the true decaying signal magnitude, which can be efficiently implemented when the phase variation is linear in time. The performance of the algorithms is demonstrated via Monte Carlo simulations, by comparing the accuracy of the estimates. Furthermore, it is shown that using one of the proposed algorithms enables more accurate T-2 estimates;in particular, phase corrected data significantly reduces the estimation bias in multi-component T-2 relaxometry example, compared to when using magnitude data. WELPE is also applied to a 32-echo in vivo brain dataset, to show its practical feasibility. (C) 2014 Elsevier Inc. All rights reserved.

关键词： Phase correction parameter estimation algorithm Multi-echo T-2 relaxometry Multi-component T-2 In-vivo brain data

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Hidden Behavior Prediction of Complex Systems Based on Hybrid Information

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IEEE TRANSACTIONS ON CYBERNETICS 2013年第2期43卷 402-411页

作者： Zhou, Zhi-Jie Hu, Chang-Hua Zhang, Bang-Cheng Xu, Dong-Ling Chen, Yu-Wang High Tech Inst Xian Xian 710025 Peoples R China Changchun Univ Technol Sch Mechatron Engn Changchun 130012 Peoples R China Univ Manchester Manchester Business Sch Manchester M15 6PB Lancs England

It is important to predict both observable and hidden behaviors in complex engineering systems. However, compared with observable behavior, it is often difficult to establish a forecastingmodel for hidden behavior. The existing methods for predicting the hidden behavior cannot effectively and simultaneously use the hybrid information with uncertainties that include qualitative knowledge and quantitative data. Although belief rule base (BRB) has been employed to predict the observable behavior using the hybrid information with uncertainties, it is still not applicable to predict the hidden behavior directly. As such, in this paper, a new BRB-based model is proposed to predict the hidden behavior. In the proposed BRB-based model, the initial values of parameters are usually given by experts, thus some of them may not be accurate, which can lead to inaccurate prediction results. In order to solve the problem, a parameter estimation algorithm for training the parameters of the forecasting model is further proposed on the basis of maximum likelihood algorithm. Using the hybrid information with uncertainties, the proposedmodel can combine together with the parameter estimation algorithm and improve the forecasting precision in an integrated and effective manner. A case study is conducted to demonstrate the capability and potential applications of the proposed forecasting model with the parameter estimation algorithm.

关键词： Belief rule base (BRB) hidden behavior hybrid information Maximum likelihood (ML) parameter estimation algorithm prediction uncertainty

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