The Experts below are selected from a list of 22380 Experts worldwide ranked by ideXlab platform

Hung Nguyen - One of the best experts on this subject based on the ideXlab platform.

  • WCNC - Improving QPSK demodulator performance for quadrature receiver with information from amplitude and phase imbalance correction
    2000 IEEE Wireless Communications and Networking Conference. Conference Record (Cat. No.00TH8540), 2000
    Co-Authors: Hung Nguyen
    Abstract:

    In communication receivers, the gain and phase inbalance between the inphase and quadrature (I&Q) channels can cause serious degradation in receiver performance. Consider the problem of jointly estimating the four parameters of the received I&Q signals: the amplitude imbalance, the DC offset for each channel, and the phase onsets between the I&Q channels. Using a Fourier series expansion of the received data, the equations for Least Square Estimation based on the measured I&Q data in a loop back mode are derived. The estimated parameters are then used for the correction of the data and the improvement of the BER performance. Examples are given for the compensation of amplitude and phase imbalance in a QPSK receiver and higher order modulation.

  • improving qpsk demodulator performance for quadrature receiver with information from amplitude and phase imbalance correction
    Wireless Communications and Networking Conference, 2000
    Co-Authors: Hung Nguyen
    Abstract:

    In communication receivers, the gain and phase inbalance between the inphase and quadrature (I&Q) channels can cause serious degradation in receiver performance. Consider the problem of jointly estimating the four parameters of the received I&Q signals: the amplitude imbalance, the DC offset for each channel, and the phase onsets between the I&Q channels. Using a Fourier series expansion of the received data, the equations for Least Square Estimation based on the measured I&Q data in a loop back mode are derived. The estimated parameters are then used for the correction of the data and the improvement of the BER performance. Examples are given for the compensation of amplitude and phase imbalance in a QPSK receiver and higher order modulation.

Jann N Yang - One of the best experts on this subject based on the ideXlab platform.

  • sequential non linear Least Square Estimation for damage identification of structures with unknown inputs and unknown outputs
    International Journal of Non-linear Mechanics, 2007
    Co-Authors: Jann N Yang, Hongwei Huang
    Abstract:

    Abstract The detection of structural damages real-time on-line, based on vibration data measured from sensors, is an important but challenging research topic, and it has received considerable attentions recently. Due to practical limitations, it is highly desirable to install as few sensors as possible in the structural health monitoring system, leading to incomplete measurements of structural responses and excitations. The traditional time-domain analysis techniques, such as the Least-Square Estimation (LSE) method and the extended Kalman filter (EKF) approach, require that all the external excitations (inputs) be available, which may not be the case for most structural health monitoring systems. Recently, the adaptive sequential non-linear Least-Square estimate (SNLSE) method has been proposed for the on-line identification of structural damages. In this paper, we extend the SNLSE method to cover the general case with unknown (unmeasured) excitations (inputs) and unknown (unmeasured) acceleration responses (outputs) in order to reduce the number of sensors required in the structural health monitoring system, referred to as the SNLSE-UI-UO. Analytic recursive solutions for the new approach are derived and presented. The accuracy and effectiveness of the proposed approach have been demonstrated using the Phase I ASCE structural health monitoring benchmark building, a 5-degree-of-freedom non-linear hysteretic building model, and a 3-story steel frame finite-element model. Simulation results indicate that the proposed approach is capable of tracking the changes of structural parameters leading to the identification of damages.

  • sequential non linear Least Square Estimation for damage identification of structures
    International Journal of Non-linear Mechanics, 2006
    Co-Authors: Jann N Yang, Hongwei Huang, Silian Lin
    Abstract:

    Abstract An early detection of structural damage is an important goal of any structural health monitoring system. In particular, the ability to detect damages on-line, based on vibration data measured from sensors, will ensure the reliability and safety of the structures. In this connection, innovative data analysis techniques for the on-line damage detection of structures have received considerable attentions recently, although the problem is quite challenging. In this paper, we proposed a new data analysis method, referred to as the sequential non-linear Least-Square (SNLSE) approach, for the on-line identification of structural parameters. This new approach has significant advantages over the extended Kalman filter (EKF) approach in terms of the stability and convergence of the solution as well as the computational efforts involved. Further, an adaptive tracking technique recently proposed has been implemented in the proposed SNLSE to identify the time-varying system parameters of the structure. The accuracy and effectiveness of the proposed approach have been demonstrated using the Phase I ASCE structural health monitoring benchmark building, a non-linear elastic structure and non-linear hysteretic structures. Simulation results indicate that the proposed approach is capable of tracking on-line the changes of structural parameters leading to the identification of structural damages.

  • on line identification of non linear hysteretic structures using an adaptive tracking technique
    International Journal of Non-linear Mechanics, 2004
    Co-Authors: Jann N Yang
    Abstract:

    Abstract System identification and damage detection based on vibration data have received considerable attention recently because of their importance to structural health monitoring. Various technical approaches have been proposed in the literature; however, the on-line identification of the changes of parameters for non-linear structures due to damages is still a challenging problem. In this paper, we propose an on-line adaptive tracking technique, based on the Least-Square Estimation, to identify the system parameters and their changes of non-linear hysteretic structures. The method proposed is capable of tracking abrupt or slow changes of the system parameters from which the damage event and the severity of the structural damage can be detected and evaluated. Simulation results for tracking the parametric changes of non-linear hysteretic structures are presented to demonstrate the application and effectiveness of the proposed technique in detecting the structural damages.

Hongwei Huang - One of the best experts on this subject based on the ideXlab platform.

  • sequential non linear Least Square Estimation for damage identification of structures with unknown inputs and unknown outputs
    International Journal of Non-linear Mechanics, 2007
    Co-Authors: Jann N Yang, Hongwei Huang
    Abstract:

    Abstract The detection of structural damages real-time on-line, based on vibration data measured from sensors, is an important but challenging research topic, and it has received considerable attentions recently. Due to practical limitations, it is highly desirable to install as few sensors as possible in the structural health monitoring system, leading to incomplete measurements of structural responses and excitations. The traditional time-domain analysis techniques, such as the Least-Square Estimation (LSE) method and the extended Kalman filter (EKF) approach, require that all the external excitations (inputs) be available, which may not be the case for most structural health monitoring systems. Recently, the adaptive sequential non-linear Least-Square estimate (SNLSE) method has been proposed for the on-line identification of structural damages. In this paper, we extend the SNLSE method to cover the general case with unknown (unmeasured) excitations (inputs) and unknown (unmeasured) acceleration responses (outputs) in order to reduce the number of sensors required in the structural health monitoring system, referred to as the SNLSE-UI-UO. Analytic recursive solutions for the new approach are derived and presented. The accuracy and effectiveness of the proposed approach have been demonstrated using the Phase I ASCE structural health monitoring benchmark building, a 5-degree-of-freedom non-linear hysteretic building model, and a 3-story steel frame finite-element model. Simulation results indicate that the proposed approach is capable of tracking the changes of structural parameters leading to the identification of damages.

  • sequential non linear Least Square Estimation for damage identification of structures
    International Journal of Non-linear Mechanics, 2006
    Co-Authors: Jann N Yang, Hongwei Huang, Silian Lin
    Abstract:

    Abstract An early detection of structural damage is an important goal of any structural health monitoring system. In particular, the ability to detect damages on-line, based on vibration data measured from sensors, will ensure the reliability and safety of the structures. In this connection, innovative data analysis techniques for the on-line damage detection of structures have received considerable attentions recently, although the problem is quite challenging. In this paper, we proposed a new data analysis method, referred to as the sequential non-linear Least-Square (SNLSE) approach, for the on-line identification of structural parameters. This new approach has significant advantages over the extended Kalman filter (EKF) approach in terms of the stability and convergence of the solution as well as the computational efforts involved. Further, an adaptive tracking technique recently proposed has been implemented in the proposed SNLSE to identify the time-varying system parameters of the structure. The accuracy and effectiveness of the proposed approach have been demonstrated using the Phase I ASCE structural health monitoring benchmark building, a non-linear elastic structure and non-linear hysteretic structures. Simulation results indicate that the proposed approach is capable of tracking on-line the changes of structural parameters leading to the identification of structural damages.

Zheng Bao - One of the best experts on this subject based on the ideXlab platform.

  • an adaptively weighted Least Square Estimation method of channel mismatches in phase for multichannel sar systems in azimuth
    IEEE Geoscience and Remote Sensing Letters, 2014
    Co-Authors: Yanyang Liu, Taoli Yang, Zheng Bao
    Abstract:

    Multichannel synthetic aperture radar (SAR) systems in azimuth can achieve high-resolution and wide-swath imaging. However, the quality of final SAR image can be degraded by the channel mismatch in phase which increases the energy outside the processed Doppler bandwidth (PDB). To address this problem, a calibration algorithm is proposed in this letter by minimizing the energy outside the PDB. Theoretical analysis shows that the presented method can be interpreted as an adaptively weighted Least Square Estimation problem, where the weights are related to the signal-to-noise ratio (SNR) of the echoes from different directions. Simulation results reveal that our method outperforms the conventional methods in the case of quasi-uniform sampling, particularly at the low-SNR region.

Silian Lin - One of the best experts on this subject based on the ideXlab platform.

  • sequential non linear Least Square Estimation for damage identification of structures
    International Journal of Non-linear Mechanics, 2006
    Co-Authors: Jann N Yang, Hongwei Huang, Silian Lin
    Abstract:

    Abstract An early detection of structural damage is an important goal of any structural health monitoring system. In particular, the ability to detect damages on-line, based on vibration data measured from sensors, will ensure the reliability and safety of the structures. In this connection, innovative data analysis techniques for the on-line damage detection of structures have received considerable attentions recently, although the problem is quite challenging. In this paper, we proposed a new data analysis method, referred to as the sequential non-linear Least-Square (SNLSE) approach, for the on-line identification of structural parameters. This new approach has significant advantages over the extended Kalman filter (EKF) approach in terms of the stability and convergence of the solution as well as the computational efforts involved. Further, an adaptive tracking technique recently proposed has been implemented in the proposed SNLSE to identify the time-varying system parameters of the structure. The accuracy and effectiveness of the proposed approach have been demonstrated using the Phase I ASCE structural health monitoring benchmark building, a non-linear elastic structure and non-linear hysteretic structures. Simulation results indicate that the proposed approach is capable of tracking on-line the changes of structural parameters leading to the identification of structural damages.