Weinstein Ehud

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Weinstein
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Ehud
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  • Technical Report
    Optimal multiple source location via the EM algorithm
    (Woods Hole Oceanographic Institution, 1986-07) Feder, Meir ; Weinstein, Ehud
    A computationally efficient scheme for multiple-source location estimation, based on the Estimate-Maximize (EM) algorithm, is presented. The proposed scheme is optimal in the sense that it converges iteratively to the exact Maximum Likelihood estimate of all source location parameters simultaneously. Versions of the algorithm that incorporate the estimation of the unknown amplitude attenuations and the estimation of the unknown signal waveforms are also presented.
  • Technical Report
    Multiple source location estimation using the EM Algorithm
    (Woods Hole Oceanographic Institution, 1986-07) Weinstein, Ehud ; Feder, Meir
    We present a computationally efficient scheme for multiple source location estimation based on the EM Algorithm. The proposed scheme is optimal in the sense that it converges iteratively to the exact Maximum Likelihood estimate for all the unknown parameters simultaneously. The method can be applied to a wide range of problems arising in signal and array processing.
  • Technical Report
    Optimal source localization and tracking using arrays with uncertainties in sensor locations
    (Woods Hole Oceanographic Institution, 1989-08) Segal, Mordechai ; Weinstein, Ehud
    We develop a computationally efficient iterative algorithm for source localization and tracking using active/passive arrays with uncertainties in sensor locations. We suppose that the available data consist of time delay, or differential time delay, measurements of the signal wavefront across the array. We consider a general senario in which the array uncertainties may be correlated in time and in space. The proposed algorithm is optimal in the sense that it converges montonically to the Maximum Likelihood (ML) estimate of the source trajectory parameters. In the case of multiple sources, the algorithm makes an essential use of the information available from all sources to reduce the array uncertainties (the so-called array callibration) and thus to improve the localization accuracy of each signal source. We also derive new expressions for the log-likelihood gradient, the Hessian, and the Fisher's information matrix, that may be used for efficient implementation of gradient based algorithms, and for assessing the mean square error of the resulting ML parameter estimates.
  • Technical Report
    Multipath time-delay estimation via the EM algorithm
    (Woods Hole Oceanographic Institution, 1987-01) Feder, Meir ; Weinstein, Ehud
    We consider the application of the EM algorithm to the multipath time delay estimation problem. The algorithm is developed for the case of deterministic (known) signals, as well as for the case of wide-sense stationary Gaussian signals.
  • Technical Report
    Universal criteria for blind deconvolution
    (Woods Hole Oceanographic Institution, 1990-02) Shalvi, Ofir ; Weinstein, Ehud
    We present necessary and sufficient conditions for blind equalization/deconvolution (without observing the input) of an unknown, possible non-minimum phase linear time invariant system (channel). Based on that, we propose a family of optimization criteria and prove that their solution correspond to the desired response. These criteria, and the associated gradient-search algorithms, involve the computation of high order cumulants. The proposed criteria are universal in the sense that they do not impose any restrictions on the probability distrbution of the input symbols. We also address the problem of additive noise in the system and show that in several important cases, e.g. when the additive noise is Gaussian, the proposed criteria are unaffected.
  • Technical Report
    Performance analysis of time-delay estimation systems
    (Woods Hole Oceanographic Institution, 1984-08) Weinstein, Ehud
    This is the second part of a study which deals with the problem of passive time delay estimation. The focus here is on systems employing wideband signals and/or arrays of very widely separated receivers. A modified (improved) version of the Ziv-Zakai lower bound (ZZLB) is used to analyze the effect of additive noise and signal ambiguities on the attainable mean square estimation errors. When the lower bound is plotted as a function of signal-to-noise ratio (SNR) one observes two distinct threshold phenomena dividing the SNR domain into three disjointed segments: at high SNR the lower bound coincides with the Cramer-Rae lower bound (CRLB). This is the ambiguity-free mode of operation where differential delay estimation is subject only to local errors. At moderate SNR (between the two thresholds), the lower bound exceeds the CRLB by a factor of 12(ω0/W)2 where ω0 and W are, respectively, the center frequency and signal bandwidth. In this region the ambiguities in the received signal phases cannot be resolved, however a useful estimate of the differential delay can still be obtained using the received signal envelopes. At low SNR, the lower bound approaches a constant level depending only on the variance of the a-priori search domain of the unknown delay parameter. In this region signal observations are subject to envelope ambiguities as well, thus essentially useless for the delay estimation.
  • Technical Report
    Time delay estimation in stationary and non-stationary environments
    (Woods Hole Oceanographic Institution, 1988-07) Segal, Mordechai ; Weinstein, Ehud
    We develop computationally efficient iterative algorithms for finding the Maximum Likelihood estimates of the delay and spectral parameters of a noise-like Gaussian signal radiated from a common point source and observed by two or more spatially separated receivers. We first consider the stationary case in which the source is stationary (not moving) and the observed signals are modeled as wide sense stationary processes. We then extend the scope by considering a non-stationary (moving) source radiating a possible non-stationary stochastic signal. In that context, we address the practical problem of estimation given discrete-time observations. We also present efficient methods for calculating the Jog-likelihood gradient (score), the Hessian, and the Fisher's information matrix under stationary and non-stationary conditions.