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The performance of the MUSIC and ML methods is studied, and their statistical efficiency is analyzed. The Cramer-Rao bound (CRB) for the estimation problems is derived, and some useful properties of the CRB covariance matrix are established. The relationship between the MUSIC and ML estimators is investigated as well. A numerical study is reported of the(More)
—We address the problem of designing jointly optimum linear precoder and decoder for a MIMO channel possibly with delay-spread, using a weighted minimum mean-squared error (MMSE) criterion subject to a transmit power constraint. We show that the optimum linear precoder and decoder diagonalize the MIMO channel into eigen subchannels, for any set of error(More)
—The Capon beamformer has better resolution and much better interference rejection capability than the standard (data-independent) beamformer, provided that the array steering vector corresponding to the signal of interest (SOI) is accurately known. However, whenever the knowledge of the SOI steering vector is imprecise (as is often the case in practice),(More)
In this paper we introduce a new paradigm for the design of transmitter space-time coding that we refer to as linear precoding. It leads to simple closed form solutions for transmission over frequency selective multiple-input multiple-output (MIMO) channels , which are scalable with respect to the number of antennas, size of the coding block and transmit(More)
We consider least squares (LS) approaches for locating a radiating source from range measurements (which we call R-LS) or from range-difference measurements (RD-LS) collected using an array of passive sensors. We also consider LS approaches based on squared range observations (SR-LS) and based on squared range-difference measurements (SRD-LS). Despite the(More)
These notes are based on Sections 1-4 and Appendix A in [1] which describes the COMET (COvariance Matching Estimation Techniques) method for estimating the parameters of a signal model. The derivation of the method is based on EXIP (Extended invariance principle). Here the model and the parameters that is going to be estimated are introduced. Some notations(More)