Jacob Sheinvald

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We present a new technique for localizing multiple narrowband sources by a passive sensor array based on dimensionality-reducing time-varying preprocessing of the sensor outputs. The technique allows a significant reduction in the number of receivers required for the implementation with only two receivers sufficing in the extreme case. The estimation method(More)
We present a new least-squares-based approach for the joint diagonalization problem arising in blind beamforming. The resulting estimation criterion turns out to coincide with that proposed by Cardoso and Souloumaic (see IEE Proc. F, Radar Signal Process., vol.140, no.6, p.362-70, Dec. 1993) on intuitive grounds, thus establishing the optimality of their(More)
Subspace-based line detection (SLIDE) is a novel approach for straight line fitting that has recently been suggested by Aghajan and Kailath. It is based on an analogy made between a straight line in an image and a planar propagating wavefront impinging on an array of sensors. Efficient sensor array processing algorithms are used to detect the parameters of(More)
In a recent paper, Sheinvald compared a cumulant matching criterion to three simplified criteria that he claimed to be equivalent. We show that two simplified criteria can be viewed as direct consequences of a result already published and that their equivalence must be revised. We also give additional references of closely related works that have been(More)
We consider the problem of localizing multiple narrow-band stationary signals using an arbitrary time-varying array such as an array mounted on a moving platform. We assume a Gaussian stochastic model for the received signals and employ the Generalized Least Squares (GLS) estimator to get an asymptotically-e cient estimation of the model parameters. In case(More)