The Experts below are selected from a list of 35433 Experts worldwide ranked by ideXlab platform

A. Monakov - One of the best experts on this subject based on the ideXlab platform.

  • asymptotically optimum estimation of Signal Waveform in the presence of uncertainties about the steering vector
    Signal Processing, 2013
    Co-Authors: A. Monakov
    Abstract:

    Abstract The problem of Signal Waveform estimation using an antenna array in case of uncertainties about the steering vector is considered. New asymptotically (in sample size) optimum estimators are derived. In contrast to the known optimal solutions, the employed method of synthesis yields non-iterative direct-form estimators. Simulation results are provided to evaluate the performance of the synthesized estimators. It is shown that the proposed asymptotic estimators perform as well as the iterative optimal estimators and they outperform the MVDR estimator in a wide range of input Signal-to-noise and uncertainty ratios.

  • Signal Waveform estimation in the presence of uncertainties about the steering vector
    IEEE Transactions on Signal Processing, 2004
    Co-Authors: Olivier Besson, A. Monakov, C. Chalus
    Abstract:

    We consider the problem of Signal Waveform estimation using an array of sensors where there exist uncertainties about the steering vector of interest. This problem occurs in many situations, including arrays undergoing deformations, uncalibrated arrays, scattering around the source, etc. In this paper, we assume that some statistical knowledge about the variations of the steering vector is available. Within this framework, two approaches are proposed, depending on whether the Signal is assumed to be deterministic or random. In the former case, the maximum likelihood (ML) estimator is derived. It is shown that it amounts to a beamforming-like processing of the observations, and an iterative algorithm is presented to obtain the ML weight vector. For random Signals, a Bayesian approach is advocated, and we successively derive an (approximate) minimum mean-square error estimator and maximum a posteriori estimators. Numerical examples are provided to illustrate the performances of the estimators.

  • Signal Waveform estimation in the presence of uncertainties about the steering vector
    The Thrity-Seventh Asilomar Conference on Signals Systems & Computers 2003, 1
    Co-Authors: Olivier Besson, A. Monakov, C. Chalus
    Abstract:

    The problem of Signal Waveform estimation using an array of sensors where there exist uncertainties about the steering vector of interest is presented in this paper. This problem occurs in many situations, including arrays undergoing deformations, uncalibrated arrays, scattering around the source, etc. In this paper, we assume that some statistical knowledge about the variations of the steering vector is available. Within this framework, two approaches are proposed depending on whether the Signal is assumed to be deterministic or random. In the former case, the maximum likelihood estimator is derived. It is shown that it amounts to a beamforming-like processing of the observations and an iterative algorithm is presented to obtain the ML weight vector. For random Signals, a Bayesian approach is advocated and we derive maximum a posteriori estimators. Numerical examples are provided to illustrate the performances of the estimators.

C. Chalus - One of the best experts on this subject based on the ideXlab platform.

  • Signal Waveform estimation in the presence of uncertainties about the steering vector
    IEEE Transactions on Signal Processing, 2004
    Co-Authors: Olivier Besson, A. Monakov, C. Chalus
    Abstract:

    We consider the problem of Signal Waveform estimation using an array of sensors where there exist uncertainties about the steering vector of interest. This problem occurs in many situations, including arrays undergoing deformations, uncalibrated arrays, scattering around the source, etc. In this paper, we assume that some statistical knowledge about the variations of the steering vector is available. Within this framework, two approaches are proposed, depending on whether the Signal is assumed to be deterministic or random. In the former case, the maximum likelihood (ML) estimator is derived. It is shown that it amounts to a beamforming-like processing of the observations, and an iterative algorithm is presented to obtain the ML weight vector. For random Signals, a Bayesian approach is advocated, and we successively derive an (approximate) minimum mean-square error estimator and maximum a posteriori estimators. Numerical examples are provided to illustrate the performances of the estimators.

  • Signal Waveform estimation in the presence of uncertainties about the steering vector
    The Thrity-Seventh Asilomar Conference on Signals Systems & Computers 2003, 1
    Co-Authors: Olivier Besson, A. Monakov, C. Chalus
    Abstract:

    The problem of Signal Waveform estimation using an array of sensors where there exist uncertainties about the steering vector of interest is presented in this paper. This problem occurs in many situations, including arrays undergoing deformations, uncalibrated arrays, scattering around the source, etc. In this paper, we assume that some statistical knowledge about the variations of the steering vector is available. Within this framework, two approaches are proposed depending on whether the Signal is assumed to be deterministic or random. In the former case, the maximum likelihood estimator is derived. It is shown that it amounts to a beamforming-like processing of the observations and an iterative algorithm is presented to obtain the ML weight vector. For random Signals, a Bayesian approach is advocated and we derive maximum a posteriori estimators. Numerical examples are provided to illustrate the performances of the estimators.

Olivier Besson - One of the best experts on this subject based on the ideXlab platform.

  • Signal Waveform estimation in the presence of uncertainties about the steering vector
    IEEE Transactions on Signal Processing, 2004
    Co-Authors: Olivier Besson, A. Monakov, C. Chalus
    Abstract:

    We consider the problem of Signal Waveform estimation using an array of sensors where there exist uncertainties about the steering vector of interest. This problem occurs in many situations, including arrays undergoing deformations, uncalibrated arrays, scattering around the source, etc. In this paper, we assume that some statistical knowledge about the variations of the steering vector is available. Within this framework, two approaches are proposed, depending on whether the Signal is assumed to be deterministic or random. In the former case, the maximum likelihood (ML) estimator is derived. It is shown that it amounts to a beamforming-like processing of the observations, and an iterative algorithm is presented to obtain the ML weight vector. For random Signals, a Bayesian approach is advocated, and we successively derive an (approximate) minimum mean-square error estimator and maximum a posteriori estimators. Numerical examples are provided to illustrate the performances of the estimators.

  • Signal Waveform estimation in the presence of uncertainties about the steering vector
    The Thrity-Seventh Asilomar Conference on Signals Systems & Computers 2003, 1
    Co-Authors: Olivier Besson, A. Monakov, C. Chalus
    Abstract:

    The problem of Signal Waveform estimation using an array of sensors where there exist uncertainties about the steering vector of interest is presented in this paper. This problem occurs in many situations, including arrays undergoing deformations, uncalibrated arrays, scattering around the source, etc. In this paper, we assume that some statistical knowledge about the variations of the steering vector is available. Within this framework, two approaches are proposed depending on whether the Signal is assumed to be deterministic or random. In the former case, the maximum likelihood estimator is derived. It is shown that it amounts to a beamforming-like processing of the observations and an iterative algorithm is presented to obtain the ML weight vector. For random Signals, a Bayesian approach is advocated and we derive maximum a posteriori estimators. Numerical examples are provided to illustrate the performances of the estimators.

Yi Ni - One of the best experts on this subject based on the ideXlab platform.

  • Modulation Code and Signal Characteristics for Signal Waveform Modulation Optical Disc
    Japanese Journal of Applied Physics, 2010
    Co-Authors: Yi Ni
    Abstract:

    A high-efficient and novel varying-level multilevel run-length-limited modulation code is proposed for Signal Waveform modulation (SWM) optical discs. The proposed code is composed of a maximum transition run (MTR) code and a level modulation process. The MTR code is employed to realize high code rate and satisfy the requirements of channels. Level modulation is used to eliminate inappropriate codewords for SWM channels and determine the level numbers for different runs. The rate of the presented code is 7/8 bits/symbols, and the recording density parameter RBPF (recording bits per 400 nm) is 2.26, which is 50.7% more than that of a digital versatile disc (DVD). The realization of the proposed code in SWM discs and the corresponding Signal characteristics are also shown. With the run-length detection and level detection solution, the bit error rate (BER) is less than 2×10-4, which is feasible for SWM multilevel optical discs.

  • multi level read only recording using Signal Waveform modulation
    Optics Express, 2008
    Co-Authors: Yi Tang, Yi Ni, Hua Hu, Buqing Zhang
    Abstract:

    A novel multi-level read-only recording using Signal Waveform modulation (SWM) is presented. The SWM is realized by inserting a sub-pit/sub-land to the original land/pit. Numerical simulation provides a helpful tool for the write strategy optimization. This method is experimentally validated on the DVD platform. Comparing with 2-level recording, an increase of 50% in capacity can be expected. It shows superiority over Signal amplitude modulation (SAM) multi-level in replication processing, capacity increase and servo performance.

Yue Rong - One of the best experts on this subject based on the ideXlab platform.

  • APCC - Joint source and relay optimization for distributed MIMO relay system
    The 17th Asia Pacific Conference on Communications, 2011
    Co-Authors: Apriana Toding, Yue Rong, Muhammad R. A. Khandaker, Yue Rong
    Abstract:

    In this paper, we develop the optimal transmit beamforming vector and the relay amplifying factors for a multiple-input multiple-output (MIMO) relay communication system with distributed relay nodes. Using the optimal beamforming vector, an iterative joint source and relay beamforming algorithm is developed to minimize the mean-squared error (MSE) of the Signal Waveform estimation. Numerical simulations are carried out to demonstrate the performance of the proposed joint source and relay beamforming algorithm.

  • Optimal joint source and relay beamforming for MIMO relays with direct link
    IEEE Communications Letters, 2010
    Co-Authors: Yue Rong
    Abstract:

    In this letter, we investigate the optimal structure of the source precoding matrix and the relay amplifying matrix for non-regenerative multiple-input multiple-output (MIMO) relay communication systems with the direct source-destination link. We show that both the optimal source precoding matrix and the optimal relay amplifying matrix have a beamforming structure. Based on this structure, an iterative joint source and relay beamforming algorithm is developed to minimize the mean-squared error (MSE) of the Signal Waveform estimation. Numerical example demonstrates an improved performance of the proposed algorithm.

  • Linear Non-Regenerative Multicarrier MIMO Relay Communications Based on MMSE Criterion
    IEEE Transactions on Communications, 2010
    Co-Authors: Yue Rong
    Abstract:

    In this letter we propose linear non-regenerative multicarrier multiple-input multiple-output (MIMO) relay technique that aims to minimize the mean-squared error (MSE) of the Signal Waveform estimation at the destination. We generalize the existing result on the structure of the optimal relay amplifying matrix by considering the direct source-destination link. To minimize the MSE, a power loading algorithm is developed which has a significantly reduced computational complexity compared with existing techniques.